“Pretrial Release: The Illusion of Algorithmic Neutrality” is the first installment in a series I’m calling Detained by Design: Algorithmic Justice and the Erosion of Constitutional Bail in California.

Over the coming posts, I’ll examine how California’s pretrial release system — shaped by reform-minded case law, bolstered by risk assessment tools, and framed by laws like SB 36 — has quietly changed into something that may look data-driven and fair, but too often behaves like its shadowy (and unaccountable) punitive predecessor.

How the Series Will Work

This series is grounded in both empirical and lived experience. On the empirical side, I’ll draw briefly from recent studies — including a 2025 Apple paper, along with related work by Microsoft and others — that reveal a core limitation in how large reasoning models perform as the complexity of a task increases. These studies matter because they mirror how our courts increasingly rely on Artificial Intelligence (AI) models and tools that perform well in easy cases but degrade—sometimes silently — when nuance is needed most.

On the ground, because I primarily practice criminal defense in these four counties, I’ll be filing public records requests in Fresno, Kings, Tulare, and Madera counties to better understand how SB 36 is being implemented in practice. The public has been told these tools are transparent, validated, and fair. I intend to find out whether that holds up under scrutiny.

The series itself may occasionally be interrupted by other blog posts, but the structure will remain intact: each entry will begin with “Pretrial Release:” at the start of each post title, each will note its place in the series, and every time I write a new post, I’ll go through the series and update links. This way, all posts will contain obvious connections to all others for readers who arrive later in the series, or anyone, really, who wants to read the entire set of posts.

Think of this post as the introduction, the roadmap, and the invitation to follow along.

The Map of the Road Ahead

Each post in this series takes up a specific fault line in the system. Here’s what the entire series will cover:

1. Pretrial Release: The Illusion of Algorithmic Neutrality
   — This post, which introduces the series, its themes, and its framing.
2. Pretrial Release: When the Risk Tools Fail the Risky Cases
   — A look at how and why algorithmic risk assessments collapse under complex case conditions, drawing from recent research in large reasoning models.
3. Pretrial Release: Silent Overrides and the Disappearing Record
   — A closer examination of judicial behavior in counties like Fresno, where overrides of algorithmic recommendations happen frequently and without explanation.
4. Pretrial Release: A Law Written in Light, Enforced in Shadow
   — An analysis of SB 36, how it was supposed to promote transparency and accountability, and how it’s functionally failing to deliver either.
5. Pretrial Release: Humphrey, Hollowed Out
   — Revisiting California’s landmark Humphrey decision and showing how its promise of individualized, non-punitive bail review is being eroded by silent automation and procedural sleight of hand.
6. Pretrial Release: When Algorithms and Institutions Protect Each Other
   — How judicial discretion and algorithmic scoring create a self-reinforcing loop, with each obscuring the flaws of the other.
7. Pretrial Release: Resisting the Conveyor Belt
   — The final post in the series, where I propose paths forward—from policy reforms to public records strategies to trial-level defense tactics that challenge the current system’s structural failures.

Each post will include updated links to the rest of the series, so readers who enter at any point can still follow the full arc. Even if I pause to write on other topics along the way, the structure, titles, and focus of this series will remain consistent and linked so it will be easy to find all the posts.

Why “Neutrality” is the Wrong Frame

One of the great myths of modern legal reform is the idea that algorithms are neutral. That they take the bias out of human decision-making. That they bring consistency to a process distorted by subjectivity. But this way of framing it collapses under scrutiny — especially in the pretrial context.

I’ve written about this most recently in Confabulations Cause Hallucinations: AI Lies Fool More Than Our Eyes.

Although the commonly-used risk assessment tools in California do not utilize AI — they’re actuarial tools, or statistic scoring systems based on pre-selected variables and logistic regression —they suffer the same basic defect. They don’t see people. They process proxies; that is, they look at simplified data points like age, arrest history, prior failures to appear, then presume this is the whole story. They don’t know, for example, if the person had no transportation, no child care, or never received proper notice regarding their case or court hearing. (They might not even know they had a case until they got picked up on an arrest warrant during a traffic stop!)

Relatedly, though often touted as capable of predicting an individual’s likelihood of pretrial success, the tools are actually incapable of making individualized predictions. They instead study data from many individuals and then forecast aggregate group risk. A risk score therefore indicates that a person shares traits with a group who succeeded or failed at a certain rate. But the score provides no information about how a specific individual will behave if released.

— Brandon Buskey & Andrea Woods, Making Sense of Pretrial Risk Assessments, Champion, June 2018, at 18.

These actuarial risk assessment tools don’t understand the dynamics of systemic racism, or why someone might have multiple bench warrants for failing to appear while navigating housing instability or mental illness. These tools are built on decades of data that reflect biased policing, charging, and prosecutorial discretion. In that sense, like AI systems, they’re not neutral — they’re feedback loops. They feed our own biases back to us.

But it’s worse than that. The tools break down under pressure. Recent studies relating to AI systems — including a 2025 paper out of Apple and complementary work from Microsoft — have shown that large reasoning models perform less effectively as problem complexity increases. They give the appearance of deliberation in simple cases, but once multiple contingencies or contradictory variables enter the picture, their reasoning process falters. The model doesn’t warn the user that it’s failing. It just proceeds — confidently wrong.

The same is true with the actuarial models used for pretrial risk assessment, only more so, because there is no AI to start with: just bare algorithms built on biased information (data).

And it’s not just the tool that fails. It’s the whole criminal justice system around it. A 2023 field study in Pennsylvania — aptly titled Ghosting the Machine — documented how judges routinely dismissed their state’s risk assessment tool, describing it as “useless,” “not helpful,” or simply ignoring it altogether without explanation.

In addition,

human decision-makers can selectively follow algorithmic recommendations to the detriment of individuals already likely to be targets of discrimination. In Kentucky, a pretrial risk assessment tool — intended as a bail reform measure — increased racial disparities in pretrial releases and ultimately did not increase the number of releases overall because judges ignored leniency recommendations for Black defendants more often than for similar white defendants. Likewise, judges using a risk assessment instrument in Virginia sentenced Black defendants more harshly than others with the same risk score.

— Dasha Pruss, Ghosting the Machine: Judicial Resistance to a Recidivism Risk Assessment Instrument, in ACM Conference on Fairness, Accountability, and Transparency (FAccT) (June 2023), https://doi.org/10.1145/3593013.3593999.

While California’s legal framework differs, the behavioral pattern feels familiar. In counties like Fresno, overrides of algorithmic recommendations are common, but rarely recorded. SB 36 was supposed to expose this kind of behavior through transparency and reporting. But in practice, it shields it behind procedural abstractions and missing data.

The Pennsylvania study reminds us that the existence of a tool doesn’t ensure its meaningful use—especially when the institution wielding it prefers opacity over constraint.

This is why “neutrality” is the wrong frame. What we’re dealing with, even after SB 36, isn’t a neutral system flawed by implementation. It’s a system designed to look “reformed” while preserving discretion, avoiding accountability, and automating the logic of preemptive incarceration. The tool is a prop.

The override is the real policy.

What’s Coming Next

In the next post — Pretrial Release: When the Risk Tools Fail the Risky Cases — I’m going to dig into the technical breakdown behind much of what I’ve covered so far.

I’ll explain why these tools — while simple and fast in low-stakes cases — start to fall apart when complexity enters the picture. (If you read it, this is reminiscent of what Apple’s June 2025 paper found for AI. As I noted above, this same difficulty holds true for the actuarial tools behind California’s pretrial risk assessment.)

That collapse isn’t just a matter of degree: it’s structural. The collapse, in other words, flows directly from how the tools are built.

And it isn’t visible to the people relying on the output. (Frankly, I doubt most probation officers, or even judges, have even a basic understanding of these tools.) When the model fails to process nuance, it doesn’t flash red or pause for clarification. It just returns a number.

That number (sometimes) drives a detention decision, shapes judicial discretion, or “justifies” continued incarceration — all without anyone acknowledging its own unreliability. (Sometimes defense attorneys make inchoate attempts at showing this. But, frankly, most defense attorneys barely understand the defects in the tools any better than probation or the judges. We do, however, usually know more about our clients than they do.)

This isn’t a small problem. The system claims to be centered on fairness, individualized assessment, and due process. But when the tools flatten complexity and the judges either can’t see it or don’t care to, what’s really happening is the automation of preemptive punishment.

The next post will show why that’s not just a software limitation: it’s an institutional and constitutional one.

Stay tuned for the next post! Maybe a different bat time; same bat channel!

The post Pretrial Release: The Illusion of Algorithmic Neutrality appeared first on Fresno Criminal Lawyer. It was written by Rick.

If you’ve read any of my other posts on AI, you know my key concern with it: confabulations cause hallucinations.

Case in point — and I’ll come back to this later — the other day I ran across an article about a dead man “coming to court” to give a victim impact statement regarding his own murder. I sent it to a couple of friends, including Scott Greenfield, who blogged about it.

As Scott wrote in an update to that post, victims’ advocates love this new move. Nevermind that the whole thing is a total fiction.

Fictions bring convictions, after all.

Confabulation vs. Hallucination

Let’s clear up what I consider to be a philosophical-terminological, — and I don’t consider it merely semantic — distinction.

What Are Hallucinations?

As I explained in a previous blog post on artificial intelligence:

In AI circles, you’ll often hear people say that large language models “hallucinate.” I’ve used this term myself in my past posts. It’s the industry’s shorthand for when the model makes something up: a case that doesn’t exist, a quote that no one ever said, a citation to one or more books or articles that exist only for the ghosts in the machine.

— Rick Horowitz, Ghosts in the Machine: Why Language Models Seem Conscious, § Hallucinations & Confabulations (Apr. 15, 2025)

But as I also explain in that post, “hallucination” is not really the right word. “Hallucination” refers to something which the brain believes it has sensed, even though it’s not real — it’s not really “there” in the outside world.

A hallucination is a false perception of objects or events involving your senses: sight, sound, smell, touch and taste. Hallucinations seem real, but they’re not.

— Cleveland Clinic, Hallucinations, Clev. Clinic (last visited May 10, 2025)

Although “hallucinations” may rely to some degree on sensory input, they are mere figments of the imagination.

I think “confabulation” is a better explanation of what happens when an artificial intelligence produces a falsity, whether in response to a prompt, or otherwise.

Human beings might hallucinate (“see,” or otherwise sense) ghosts, but the ghosts in the machine sense nothing. They are not sentient. They do, however, produce “explanations,” some of which are pure confabulations.

What Is Confabulation?

This idea of confabulation comes to us from the field of psychology. Human beings confabulate frequently — perhaps just as frequently as LLMs. As I explained in Ghosts in the Machine, confabulation is a way of “filling in the blanks” with something that seems completely plausible. So plausible, in fact, that it is taken even by the confabulator to be true.

Most of the time, you’ll hear that confabulation has to do with memories. Often the explanation is that confabulation is a neuropsychiatric phenomenon where individuals create false memories without the intention of deception.

Confabulation occurs when individuals, including victims, witnesses, or suspects, recall events or details that did not actually happen, blending fiction with reality. Importantly, confabulation is not intentional deception or lying; rather, it arises from false or misplaced memory errors triggered by various factors such as trauma, stress, brain injuries, or cognitive impairments. In the legal context, confabulation can lead to false testimonies, wrongful accusations, and miscarriages of justice.

— Janina Cich, Navigating Confabulation: A Toolkit for Criminal Justice, Police Chief Online (Feb. 14, 2024)

But that’s not completely accurate.

Confabulation isn’t limited to faulty memory. It can show up any time we need to explain something.

For example, psychological studies make it clear that people often don’t know why they’ve done something. Yet, when asked, they produce a reason that sounds completely logical. They confabulate a justification. The brain doesn’t like to leave blanks unfilled. It prefers a tidy narrative, even if it’s made up after the fact.

Split-brain research makes this very transparent. You may have heard that the brain is divided into two “hemispheres.” And you’ll sometimes hear about the “right brain” and the “left brain” myth.

In patients whose hemispheres were surgically separated, one side of the brain was shown an image and acted on it. The other — the language-dominant side — had no way of “seeing” the image, or even knowing about it because, remember, the pathway between the hemispheres was cut. It was no longer there.

When asked why they had performed the action, the verbal hemisphere would offer a perfectly plausible explanation — entirely invented. It didn’t lie. It just had no access to the real cause and came up with a plausible story. Michael Gazzaniga coined the term “interpreter” for what the left brain does: it fills in gaps, invents explanations, spins coherent stories even when reality is fragmented or incomplete. Sound familiar?

We do this all the time. We say we bought something because it was on sale, not because we were sad. We say we felt “uneasy” about someone, not because of unconscious bias. We say we objected because of the rule, not because of pride or spite or fear.

In all these cases, we believe the stories we tell — even when they aren’t the truth. That’s the power of confabulation. It doesn’t just shape our memory. It even shapes our identity.

This “hidden” yet pervasive nature of confabulation makes it all the more dangerous when AI does it. Since we’ve trained ourselves to accept a smooth explanation as evidence of understanding and the AI explanation makes sense we’re sucked into the story. We believe it.

If you don’t understand this about AI, all kinds of things can go wrong.

The Delphic Oracle Returns, But Digitized

In an earlier post, I described ChatGPT — and other large language models — as a “Twenty-First Century Delphic Oracle.”

Like the Pythia of ancient Greece, today’s AI delivers answers in response to questions posed by mortals. But instead of inhaling volcanic fumes, it breathes in curated datasets and spits out fluently ambiguous answers that sound insightful — even when they aren’t.

That post made the case that:

• AI only appears intelligent because we’ve trained it to mimic us, essentially copying our language, our reasoning patterns, and — when applied to the law — even our legal structures.
• Its outputs are shaped entirely by its inputs and are just as biased as the curated and pre-chewed data it has been fed.
• And yet, despite the uncertainty, ambiguity, and risks, we are increasingly treating this Oracle as a source of truth.

I also noted that AI’s usefulness can’t be ignored — but neither can its limitations, especially in law. When the people building the tools don’t understand the field they’re building for, we get what Scott Greenfield called “computer nerds[,]” instead of lawyers, who write algorithms that allow AI “to play judge, jury, and executioner.”

The Hallucination of Justice: AI Invades Our Courts

Now that we’ve explored the roots of confabulation and hallucination — both human and artificial — and distinguished between them, let’s look at how AI confabulations are unleashed in real courtrooms. The results are sometimes absurd, sometimes terrifying, and always bullshit.

The Case of the Missing Cases

By now, many people have heard stories of the lawyers who get into trouble because they trusted AI. Specifically, they used Large Language Models to help them research and write briefs that they then went on to use in real courts.

They did this not knowing that some of the cases they cited and relied upon were not real cases.

In another “hard lesson learned” case, on Monday, February 24, 2025, a federal district court sanctioned three lawyers from the national law firm Morgan & Morgan for citing artificial intelligence (AI)-generated fake cases in motions in limine. Of the nine cases cited in the motions, eight were non-existent.

— Linn F. Freedman, “Lawyers Sanctioned for Citing AI-Generated Fake Cases,” Nat’l L. Rev. (Apr. 25, 2023)

Yikes! Just another reminder of why I don’t let AI help me do legal research or write briefs for me.

That said, all the major legal research tools are now infected with AI. So are police reports and even “discovery” provided by prosecutors in criminal cases.

The Case of the Avatar Defendant

A few weeks ago, Jerome Dewald represented himself in court.

Dewald supposedly has a background in engineering and computer science, but he’s not a lawyer. He owns a company — an AI startup called Pro Se Pro — that aims to help people represent themselves in court without lawyers.

Dewald’s request[ed] to play a video arguing his case, as according to him a medical condition had left the entrepreneur unable to easily address the court verbally in person at length. The panel was not expecting a computer-imagined person to show up, however.

— Thomas Claburn, AI entrepreneur sent avatar to argue in court – and the judge shut it down fast (Apr. 9, 2025)

In this case, had the judge allowed the video to go on, the result might have just been odd. Despite his “engineering and computer science” background, Dewald had been unable to properly create the avatar of himself. He used a generic avatar named “Jim” instead.

The court was understandably confused and asked who this “new” person was that was addressing the court.

Suffice it to say the court was not pleased with the explanation. The judges believed — wrongly according to the founder of the AI startup (Dewald) — that it was a publicity stunt to help advertise his AI startup by using an “AI lawyer.”

“Beyond” Forgiveness

The court hearing Dewald’s case disallowed his avatar. Another court, however, did something even more aburd: they allowed the introduction of a total fiction: an AI-generated representation of a murder victim who “appeared” in court from beyond the grave to forgive his murderer.

“To Gabriel Horcasitas, the man who shot me, it is a shame we encountered each other that day in those circumstances,” says a video recording of Pelkey. “In another life, we probably could have been friends.

“I believe in forgiveness, and a God who forgives. I always have, and I still do,” Pelkey continues, wearing a grey baseball cap and sporting the same thick red and brown beard he wore in life.

— Cy Neff, AI of dead Arizona road rage victim addresses killer in court (May 6, 2025)

Just one problem. Well, okay, a lot of problems. First, the “man” in the video wasn’t Pelkey: it was an AI-generated version of him. Second, it wasn’t Pelkey’s voice: it was an AI-generated version of it. And, third, those weren’t Pelkey’s words. The article doesn’t say who wrote the words. It might have been Pelkey’s sister; it might have been an AI.

In any event, every part of the video was a fictionalized representation of what someone else thought Pelkey might have said, if he could really have returned from the beyond to appear on court.

Talk about confabulation!

Difface: Seeing What Isn’t There

I was already going to write this post because of the Pelkey case I just told you about.

Then my friend, Mike Hamilton, posted an article about AI generating people’s faces using just their DNA.

Chinese scientists fed a string of genetic code into an artificial intelligence model. What came out looked like a face — a strikingly realistic, three-dimensional face. No photograph had ever been taken. No sketch artist had drawn it. The only source was DNA.

— Tibi Puiu, A New AI Tool Can Recreate Your Face Using Nothing But Your DNA (May 9, 2025)

But, as another (misleadingly titled) article points out, all is not hunky-dory.

Despite this, the team brought up some challenges with developing Difface, such as a limited knowledge of facial genetics and technological difficulties with high-dimensional data and a small sample size. They believe that Difface’s underlying structure is flexible enough to apply it to different ethnic groups, so they hope that expanding the database to include individuals from a wide range of ethnic backgrounds can help Difface generate accurate facial images. The team said future studies will have the opportunity to explore whether Difface needs more genetic loci to identify certain facial features, which could make the model more valuable to forensic investigations and personal medicine on a global scale. 

— Kendra Leon Barrionuevo, ‘Difface’ uses crime scene DNA to show what suspects look like (May 7, 2025)

In short, we don’t know enough about genetics and this particular experiment has other issues that make it unclear how well this tech could be used “use[] crime scene DNA to show what suspects look like.”

But, remember, fictions bring convictions. I expect it won’t be long before law enforcement tries to turn this fake headline into a reality.

When Confabulations Cause Hallucinations

Let’s not kid ourselves: the real danger isn’t just that AI lies. It’s that people believe the lies.

And not just ordinary people. Judges. Prosecutors. Jurors. Even defense lawyers sometimes.

Because the Oracle has spoken. But who is the Oracle? And how does it work?

We don’t let anonymous experts testify. We don’t let prosecutors just say “trust us” when it comes to the evidence. (Or at least, we’re not supposed to. Reality doesn’t always match this aspiration.)

But now we’ve got black box algorithms being treated like witnesses whose evidence never lies. They’re showing up in discovery, pretrial risk assessments, sentencing reports, and more. Defense attorneys have almost no way to question how they work, what they’ve been trained on, or how many false positives they’ve kicked out — in other words, how often they confabulated — in other cases.

On the plus side, some courts get it.

Courts are beginning to address such issues, including discovery in criminal cases. In an important ruling in State v. Arteaga, a New Jersey Appellate Court affirmed a trial court order, ruling that if the prosecutor planned to use FRT, or the eyewitness who selected the defendant in a photo array, then they must provide the defense with information concerning “the identity, design, specifications, and operation of the program or programs used for analysis, and the database or databases used for comparison,” as all “are relevant to FRT’s reliability.” The court concluded, the “[d]efendant must have the tools to impeach the State’s case and sow reasonable doubt.

— AI and the Criminal Justice System Working Group, Input Regarding AI and Criminal Justice, at 10 (Wilson Ctr. for Sci. & Just. June 2024) (footnotes omitted)

There’s a problem here, though. No one even knows how AI does what AI does!

It’s all a black box.

A black box AI is an AI system whose internal workings are a mystery to its users. Users can see the system’s inputs and outputs, but they can’t see what happens within the AI tool to produce those outputs.

Consider a black box model that evaluates job candidates’ resumes. Users can see the inputs—the resumes they feed into the AI model. And users can see the outputs—the assessments the model returns for those resumes. But users don’t know exactly how the model arrives at its conclusions—the factors it considers, how it weighs those factors and so on. 

Many of the most advanced machine learning models available today, including large language models such as OpenAI’s ChatGPT and Meta’s Llama, are black box AIs. These artificial intelligence models are trained on massive data sets through complex deep learning processes, and even their own creators do not fully understand how they work. 

These complex black boxes can deliver impressive results, but the lack of transparency can sometimes make it hard to trust their outputs. Users cannot easily validate a model’s outputs if they don’t know what’s happening under the hood. Furthermore, the opacity of a black box model can hide cybersecurity vulnerabilities, biases, privacy violations and other problems. 

— Matthew Kosinski, What is black box AI? (undated)

If that doesn’t scare you, it should. We’re not just talking about unreliable tools. We’re talking about the creeping illusion that the tools are truth-tellers and objective — that they don’t lie, don’t guess, don’t confabulate.

But we actually know that’s not true: they do “lie”; they do “guess”; they do confabulate. And we don’t really know because we don’t know how it all works.

My concern is that once you start to believe the Oracle, you stop questioning the fumes. You don’t even try to peek into the black boxes of their neural nets. In fact, even if a judge would order that the necessary discovery be turned over to try, how could you or anyone else figure it out?

That’s the real danger: when we forget how these systems are built — from curated datasets, full of human biases and pattern-matching tricks — and we start treating their pronouncements like prophecy.

Confabulations cause hallucinations. Not just in machines, but in us.

Conclusion: Courtrooms Must Be Places of Reality

The brave new world of artificial intelligence is already here — and it’s leaking into our courtrooms through evidence, arguments, and even the lips of the dead.

And just like in Huxley’s novel, the danger isn’t the tech itself — or I should say isn’t just the tech itself — it’s how willingly we surrender to it.

But a courtroom is not supposed to be a tech demo.

It’s not a stage for simulated empathy. Not a sandbox for black box code. It’s a place where reality matters. Where people’s lives, freedom, and futures hang in the balance. And if we don’t anchor courtroom decisions to something real — something verifiable and challengeable — then we’re not just letting machines confabulate.

We’re hallucinating temples — our courthouses — not of justice, but of AI’s confabulated “truth.”

Related Artificial Intelligence Posts

The post Confabulations Cause Hallucinations appeared first on Fresno Criminal Lawyer. It was written by Rick.

They’re not burning books — not yet. But they are burning bridges: between truth and propaganda, between equity and erasure, between a multiracial democracy and something that looks an awful lot like the country the Confederacy dreamed of.

What we’re seeing is the New Confederacy of Silence.

Crimmigration Under Fire

Let’s start with the obvious: this is not about “meritless claims.”

When Trump goes after immigration lawyers, he’s not trying to protect the integrity of the system. He’s trying to silence the people who fight for the rights of immigrants — many of them brown, many of them poor, all of them disfavored under this administration’s agenda.

The term “crimmigration” was coined to describe the collision between immigration law and the criminal legal system.

At its most basic, “crimmigration” law describes the convergence of two distinct bodies of law: criminal law and procedure with immigration law and procedure. For most of the nation’s history, these operated almost entirely free of the other. Criminal law and procedure was thought to be the province of prosecutors, criminal defense attorneys, and the state and federal judges who oversee criminal prosecutions every day. Immigration law, in contrast, was confined to immigration courts housed within the executive branch of the federal government and staffed by immigration attorneys, immigration judges, and prosecutors employed for many years by the Immigration and Naturalization Service (INS) and now the Department of Homeland Security (DHS) or Department of Justice.

— César Cuauhtémoc García Hernández, Crimmigration Law, 1 (2d ed. 2021)

But lately, it’s starting to feel more like a controlled demolition.

Just look at the memo Trump sent to Attorney General Pam Bondi. He wants the Department of Justice to pursue sanctions —professional punishment — for lawyers whose only “crime” is filing lawsuits that challenge government overreach. Think about that. The government gets sued, doesn’t like it, and its response is to punish the lawyers. That’s not just dangerous. That’s the start of fascism. (First, we kill all the lawyers.)

But let me be even clearer: this isn’t about legal ethics. It’s political retaliation. Lawyers aren’t the ones “undermining confidence in the system.” The system is doing a pretty good job of that all by itself — when it locks up asylum seekers without due process, separates families, or uses ICE raids like political theater. The lawyers are just trying to hold the line.

This goes beyond immigration law. The playbook is expanding. You speak out, you get targeted. You sue the government, you get smeared. You win in court, and suddenly your license is on the table. I’ve been a criminal defense lawyer long enough to know that when the government starts going after defense counsel for doing their jobs, we’re in dangerous territory.

And make no mistake: this is a job. But it’s also a responsibility. When they come for people with no power, someone has to stand between them and the state. And if that someone is me, I won’t be shamed or silenced. Not by a memo, a smear campaign, or a president with a vendetta.

The DEI Smokescreen

Let’s get something straight: the backlash against DEI — Diversity, Equity, and Inclusion — isn’t organic. It’s not some spontaneous groundswell of concerned citizens waking up and realizing that fairness has gone “too far.” It’s top-down. It’s coordinated. And it’s racialized to its core.

As Derrick Bell wrote in Faces at the Bottom of the Well, racism isn’t just about ignorance or hatred — it’s a tool of control. Bell argued that entrenched racial hierarchies are often useful to the wealthy and powerful because they keep marginalized groups — and working-class white people — fighting each other instead of recognizing their shared struggle against economic exploitation. When you understand that, the sudden, well-funded backlash against DEI makes a lot more sense. It’s not just about racial animus; it’s about protecting the status quo by stoking grievance and fear. If we’re busy fighting over who “deserves” fairness, we’re not paying attention to who’s hoarding all the power.

The scientific and academic communities didn’t suddenly forget why DEI matters. Quite the opposite — they’ve been leading the way in trying to correct for generations of exclusion, underrepresentation, and outright discrimination. The idea that DEI initiatives are some kind of existential threat to merit, objectivity, or “American values” is a political fiction. It’s not about protecting standards; it’s about preserving hierarchies.

And now, under Trump’s second term, that fiction is being written into policy. Scientific journals are receiving threatening letters from federal prosecutors. Universities are walking back programs designed to support underrepresented students. Even the New England Journal of Medicine is under fire — not for publishing bad science, but for publishing the wrong kind of values. The government has decided that diversity is dangerous, equity is suspicious, and inclusion is un-American.

Let’s not kid ourselves about what’s really going on here.

“DEI” has become the new stand-in for every slur and stereotype the far-right can’t say out loud anymore. It’s a more palatable, sanitized way to attack Black and brown people, immigrants, LGBTQ+ communities, and anyone else who doesn’t fit neatly into the 1950s sitcom fantasy that these folks are so desperate to resurrect. When they say they’re fighting DEI, what they’re really fighting is us. And when they claim it’s about fairness or balance or colorblindness, what they mean is they want the old system back — where whiteness meant default, and everyone else had to fight for scraps.

The idea that DEI is “divisive” is the real smokescreen. What’s actually divisive is a government using its power to punish inclusion. What’s destructive is a movement that believes civil rights went too far, and is now working overtime to reverse them.

This isn’t policy debate. It’s race politics in a new outfit. And the stitching is coming undone.

Free Speech, But Only for the Right

The right-wing’s invocation of “free speech” has become a selective shield — protecting their own narratives while silencing dissenting voices. Adam Serwer aptly describes this as the “conservative right to post,” highlighting the glaring hypocrisy: free speech is championed only when it aligns with their agenda.

This selective defense is evident in President Trump’s recent executive order to defund NPR and PBS, accusing them of ideological bias. Such actions threaten the foundational principle of press freedom and raise concerns about authoritarian overreach.

The attack extends beyond media to academic and scientific communities. Efforts to dismantle Diversity, Equity, and Inclusion (DEI) initiatives are not mere policy disagreements but are rooted in a desire to suppress discussions on systemic inequality. These initiatives aim to address historical disparities and promote inclusivity, yet they are being targeted under the guise of combating “wokeness.”

It has also frozen every single research grant at the US National Science Foundation, one of the largest funders of basic research in the world, in order to review the grants’ language for terms related to DEI. Some of the “woke” or “partisan” stuff that’s been kneecapped by the sweeping cuts include cancer and Alzheimer’s research. If the draconian measures were intended to be an out-and-out assault on the scientific community, it’s working: with the money drying up, some of the nation’s top scientists are starting to consider leaving the country.

— Frank Landymore, “The Government Is Now Threatening Academic Journals” (April 20, 2025)

This orchestrated campaign seeks to reshape the cultural and educational landscape, reminiscent of eras where dissent was stifled, and conformity was enforced. By controlling narratives and limiting platforms for alternative perspectives, there’s a concerted effort to redefine national values and history. But it will have the side effect of turning the United States into a desert wasteland of scientific progress.

In reality, though, the proclaimed defense of free speech by the right serves as a facade, masking a deeper intent to consolidate power and suppress opposition. True commitment to free expression requires safeguarding diverse voices, especially those challenging the status quo.

What’s Really at Stake

This isn’t just about policy — it’s about power: who holds it, who wields it, and who gets silenced by it. It’s about who defines what it means to be American, and who gets erased from that definition.

If Trump and his allies succeed, they won’t just rewrite the laws — they’ll rewrite the narrative. They’ll recast history to serve their agenda, marginalize communities under the guise of “restoring order,” and weaponize institutions to suppress dissent. This isn’t preservation; it’s regression — a deliberate attempt to drag us back to a time when voices like mine, and those I represent, were systematically excluded.

Sherrilyn Ifill put it plainly: “We will cease to be a democracy” if Trump returns to power.

And so it has happened and is happening.

Consider the recent shifts: The Department of Justice’s Civil Rights Division has been redirected to focus on voter fraud, sidelining its core mission of protecting voting rights. DEI initiatives are being dismantled across federal agencies, with employees purged for merely participating in diversity programs. Even cultural institutions like the National Museum of African American History and Culture are under attack, with executive orders aiming to marginalize Black history.

This is a calculated strategy to consolidate power by erasing the progress we’ve made toward inclusivity and equity. It’s not about conserving American values; it’s about redefining them to fit a narrow, exclusionary vision.

We must recognize this for what it is: an authoritarian playbook designed to suppress opposition and entrench a singular narrative. Our response must be unwavering. We must defend the principles of justice, equity, and inclusion — not just in our rhetoric, but in our actions.

Free Speech vs. the New Confederacy of Silence

The Confederacy lost the Civil War. But its ideology — white supremacy, authoritarianism, and the suppression of dissent — has never fully disappeared.

As I said in 2020,

[A]s evidenced by the short-lived and ineffective Reconstruction, then the start of the “Redemption” in 1877, and the rise of Jim Crow, the reality is that the exploitation of blacks, and the killing of both blacks and whites who opposed the “reconstructed” Confederacy did not stop.

— Rick Horowitz, The Supreme Reconstruction & Redemption of Civil Rights, 2020 Forum (Cal. Att’ys for Crim. Just.) 52, 53

Today, these ideas are re-emerging under new guises: “free speech,” “anti-DEI,” and “protecting America.” This resurgence isn’t a coincidence; it’s a calculated effort to roll back decades of civil rights progress.

In recent months, the Trump administration has aggressively targeted diversity, equity, and inclusion (DEI) initiatives, dismantling programs designed to support marginalized communities. Executive orders have revoked equal employment protections, censored educational institutions, and slashed funding for minority-owned businesses and cultural institutions. Even the Department of Defense has purged content celebrating the achievements of Black, Indigenous, and female veterans, effectively erasing their contributions from public memory.

These actions are often justified under the banner of “free speech” or combating “wokeness.” However, they represent a deliberate campaign to suppress honest historical discourse and institutionalize exclusionary policies. By attacking DEI efforts, the administration undermines not only racial justice but also violates fundamental rights to free speech and association.

As a criminal defense lawyer, I see firsthand how these policies impact the pursuit of justice. They embolden discriminatory practices and silence voices that challenge systemic inequities. As a citizen, I recognize the danger in allowing such ideologies to go unchallenged. And as someone who still believes in liberty and justice for all, I refuse to stay silent.

The Confederacy may have been defeated on the battlefield, but its legacy persists in these modern assaults on equality and truth. It’s incumbent upon all of us to confront this resurgence, to defend the principles of inclusion and justice, and to ensure that the arc of history continues to bend toward a more equitable future.

The post The New Confederacy of Silence appeared first on Fresno Criminal Lawyer. It was written by Rick.

The last few days, I’ve been in Las Vegas. Yeah, I know. But it’s not all fun and games; mostly, it’s not games at all. Since I like learning, it is fun.

The idea for this post — “Orphans in Poisoned Libraries: Training LLMs (and Children) on Racist Datasets” — comes from this experience. I attended the 2025 Forensic Science seminar put on by the National Association of Criminal Defense Lawyers, of which I’ve been a member for many years now.

Fittingly, given my recent focus, I sat through the entire “AI & Technology” track in the training.

Introduction

Anyone paying attention realizes we are living in a strange, dangerous moment. Across the country, under the banner of dismantling “DEI” — an agenda pushed by the Trump Administration to distract from the pillage, plunder, and transformation of our democratic republic into an authoritarian oligarchy — government offices are purging our people, our history, and our progress.

Websites that once documented the contributions of Black Americans, Latinos, and Native peoples are being deliberately “disappeared.”

The administration is removing data sets and rewording histories.

The misnamed “Department of Government Efficiency” (DOGE) is firing people, not because they were unqualified, but because they have become too visible. Our white nationalist government is revolting — in every sense of that word — because of their view that the United States is becoming too Black, too Brown, and too unacceptable to the New Order that demands whiteness without apology.

It’s to be expected. When regimes have sought to control the future, they have often begun by erasing the past.

The Nazis spoke of a “New Order” as they burned books, purged archives, took over university curricula, and “synchronized culture” to suit their ideology.

Today, we watch something quieter but no less dangerous: websites stripped of Black, Brown, and female histories; records erased under the banner of dismantling “DEI,” truths removed before the next generation — and the next generation of machines — can inherit them.

It would be foolish to imagine that these acts stay neatly contained.

They bleed outward. Into the stories we tell about ourselves.
Into the datasets from which future generations — including future generations of machines — will learn.

Our cultural archives — and by adding the word “cultural” I’m expanding beyond the idea of books, papers, websites, and other records to include all the structural components of our racist United States — were already poisoned. And we are now ripping out the few antidotes that remained.

And in doing so, we are making sure that when we send the next generations into the libraries, there will be even fewer honest resources for them to find. (And, oh, by the way, Trump wants to make the problem of biased, racist AI even worse.)

Orphans in Poisoned Libraries

We left orphans alone in poisoned libraries. We told them to learn everything, and then we blamed them for what they learned. LLMs like ChatGPT, Gemini, and Claude weren’t taught through careful curation. No one stood by to explain that some victories were built on the backs of others, or that some records preserved injustice rather than truth. Instead, we opened the doors to everything — to verdicts born of racial terror, to medical texts that mistook oppression for biology, to journalism that taught readers to fear certain faces. And when the orphans dutifully absorbed it all, we recoiled at the reflections they offered back.

(Children learn the same way. No one hands them a syllabus titled “Injustice and How to Perpetuate It,” but they study it all the same. They study it in the way a mother’s voice sharpens when she locks the car doors. They study it in the way certain names earn respect and others earn suspicion. They study it in what is said and what is unsaid. Like the orphans we have abandoned to poisoned libraries, children absorb the lessons we pretend not to teach.)

And this happens even when we take deliberate steps not to teach something, like how to disparately treat people on the basis of race.

“In a world where we have lots of information about every individual and a powerful machine to squeeze out a signal, it’s possible to reconstruct whether someone is part of a protected group even if you exclude that variable,” Spiess says. “And because the rules are so complex, it’s really hard to understand which input caused a certain decision. So it’s of limited use to forbid inputs.”

— Katia Savchuk, “Big Data and Racial Bias: Can That Ghost Be Removed from the Machine?” (October 28, 2019)

When the machines echo back our biases, it is not a sign of their failure. It is the surest sign of their success.

At least at learning what we really value.

The Inheritance of Racial Stereotypes

LLMs, in some sense like children, are trained not — or at least not only — by explicitly teaching them “rules” to live by. They are trained from texts. Large data sets of information.

We don’t know exactly how they learn to do what they do, but it’s essentially “automatic” upon exposure to “data.”

I’ll get into why I’ve used scare quotes around “automatic” and “data” in a bit. But the key point is that we essentially say, “Here, read this and see what you can learn.” We might be thinking — especially when it comes to LLMs — that we mean “about language.” But then we feed them Comparative Physiognomy, or Resemblances Between Men and Animals, by James W. Redfield, M.D. Or the works of Ernst Haeckel, who through

The History of Creation [which] was translated into all the major European languages — … played an active role in helping concepts regarding the inferiority of certain “races” to spread and have an impact.

— Bernd Brunner, “Human Forms in Nature: Ernst Haeckel’s Trip to South Asia and Its Aftermath” (September 13, 2017)

Nor are such ostensibly biological, medical, or scientific texts the only datasets that, while teaching LLMs language, also teach and bake in racial bias.

How about a nice diet of Dred Scott v. Sandford (1857), Plessy v. Ferguson (1896), the Civil Rights Cases (1883), Lum v. Rice (1927), or a dataset containing Jim Crow Laws (and the justification for them)? All of these, and more, reinforced “Justice” Taney’s view from Dred Scott that “so far inferior that they had no rights which the white man was bound to respect.”

Think that’s enough? What about journalism? As we know, AI is trained on large datasets of news, lifestyle, and other journalistic articles.

Yet journalism — especially historically — criminalized Black and Brown bodies by consistently portraying people of color as inherently dangerous, threatening, or lawless, regardless of individual guilt or innocence. It wasn’t just what stories were told, but how they were told, whose voices were amplified, and what narratives were normalized.

Just think back to the not-that-long-ago controversial move of darkening O.J. Simpson’s face on the cover of Time Magazine.

If we fail to consider the entire communicative ecology in which LLMs grow up, or

If we focus on one particular kind of media at the exclusion of others, if we do not look at the entire communications ecology and how it affects other pieces of how local communities get their information, then we are missing the forest for the trees[.]

— University of Southern California Professor Mark Lloyd, quoted in Joseph Torres, “Diagnosing the History of Racism in the Media” (October 13, 2022)

All these (and, as I have said, more) comprise the poisoned libraries we feed all we create, including LLMs (and our own children).

And it has an impact — even when we don’t know it.

Called a “neural network,” this mathematical system could learn tasks that engineers could never code into a machine on their own.

— Cade Metz, “Who Is Making Sure the A.I. Machines Aren’t Racist?” (March 15, 2021)

The answer to “who is making sure,” by the way, is “no one.” We’ve left alone the orphans in poisoned libraries. The result is predictable:

About six years ago, A.I. in a Google online photo service organized photos of Black people into a folder called “gorillas.” Four years ago, a researcher at a New York start-up noticed that the A.I. system she was working on was egregiously biased against Black people. Not long after, a Black researcher in Boston discovered that an A.I. system couldn’t identify her face — until she put on a white mask.

— Cade Metz, “Who Is Making Sure the A.I. Machines Aren’t Racist?” (March 15, 2021)

AI has claimed its inheritance.

Children, AI, and Learning

Large Language Models — LLMs — do not learn language in exactly the same way children do. Children acquire language through lived experience: trial and error, gesture and speech, social correction and emotional negotiation. LLMs, by contrast, are trained on massive static datasets — seas of words without living bodies behind them. In reality, they learn patterns, not meanings.

LLMs learn to predict continuations, not intentions. In that sense, they’re more like super-sized autocorrect programs than like anything human. (To be fair, there is more to it than just “autocorrect.”)

So when it comes to language itself, the analogy between children and LLMs falters. Children live their way into language; LLMs calculate their way through it.

But when we turn to culture, the analogy grows sharper. It helps us understand much about how bias, bigotry, and racism permeate the programs.

Children absorb the cultural environment they are born into — not by critical reasoning, but by exposure. They internalize assumptions about race, power, gender, and worth without ever being explicitly taught. They learn from what is praised, what is punished, what is left unspoken but universally enforced.

And so do LLMs.

When we train a model on the full sweep of human language —
with all its historical prejudices baked into law, medicine, journalism, and everyday discourse — we are not simply teaching it vocabulary. We are transmitting to it a map of human culture, with all its distortions intact.

LLMs do not understand this inheritance. They do not question it. They simply mirror it. They become fluent in the unspoken grammar of bias, just as surely as any child immersed in a poisoned cultural environment.

The difference is that with LLMs, the scale is larger, the speed is faster, and the reach is global.

And when the machines echo back our buried assumptions, they are not malfunctioning. They are doing exactly what they were built to do: learning from us.

The tragedy is not that they learn. The tragedy is what we have left behind for them to learn from. This is amplified when we turn LLMs loose to learn on their own, abandoning them like orphans in poisoned libraries. Especially when, because of our own “lived experience,” we may not even realize the library is poisoned, as when Clarifai tried to train content moderation systems with two databases: one with G-rated images that featured mostly white people and the other with pornography that featured mostly Black people.

“The data we use to train these systems matters,” Ms. Raji said. “We can’t just blindly pick our sources.”

This was obvious to her, but to the rest of the company it was not. Because the people choosing the training data were mostly white men, they didn’t realize their data was biased.

— Cade Metz, “Who Is Making Sure the A.I. Machines Aren’t Racist?” (March 15, 2021)

Prepping the Poisoned Library

This is a good point for me to deliver on my promise above to explain the scare quotes around “automatic” and “data.”

Machine Learning Isn’t Really (or Always) Automatic

First, “automatic” in the context of training AI systems is not 100% automatic. We choose the datasets on which AI systems are trained. Some are curated, cleaned, prepared; some are not. Nor is the learning process itself always automated. I’m no expert, but I know of at least two areas — HITL and RHLF — where humans insert themselves into the training.

HITL is Humans in the Loop and is a broader concept: humans may be involved in numerous different parts of the training — data labeling, model evaluation, or live system corrections. RHLF is Reinforcement Learning with Human Feedback and is more specific, involving humans ranking or rating model outputs to reinforce preferred behaviors, and then reinforcement learning algorithms adjust the model’s responses based on those ratings.

Think of it like this: HITL is like having a coach on the field during practice, giving tips whenever needed; RLHF is like having a judge after the game, scoring every move to retrain the player for next time.

There’s No Such Thing as “Raw Data”

You may have guessed this already from all I’ve said above about the orphans in poisoned libraries, but there’s no such thing as raw data.

We often say, “The data speaks for itself” or assure others that “the data doesn’t lie.” The problem, as I think I showed above, is that the data doesn’t speak for itself: it speaks for us; it mirrors our prejudices and biases. And data can, indeed, lie.

This is because,

[h]ow data are construed, recorded, and collected is the result of human decisions — decisions about what exactly to measure, when and where to do so, and by what methods. Inevitably, what gets measured and recorded has an impact on the conclusions that are drawn.

— Nick Barrowman, Why Data Is Never Raw, 56 New Atlantis 129 (Summer/Fall 2018).

And as an example of how data, then, can lie, Barrowman offers this:

For example, rates of domestic violence were historically underestimated because these crimes were rarely documented. Polling data may miss people who are homeless or institutionalized, and if marginalized people are incompletely represented by opinion polls, the results may be skewed.

— Nick Barrowman, Why Data Is Never Raw, 56 New Atlantis 129 (Summer/Fall 2018).

To be fair, Barrowman doesn’t used the word “lie.” He instead points out that “all data is cooked” and it is the cooking that can, if we aren’t careful, result in untrue or wrong results. Or, as I said, “lies.”

And unlike what I’ve tried to do here, Barrowman didn’t even get into the way that language itself is cooked. There’s no such thing as “raw language,” either.

Surface Level Corrections and Covert Bias

Thus, it’s not just a problem of picking the right datasets for training. That might have helped with the content moderation system described above, but that’s not the chief method by which cultural attitudes are passed from generation to generation or, in our case, people to LLMs.

Culture is a way of life within a society that is learned by its members and passed down from generation to generation—language plays a central role in this process of cultural reproduction.

— Yan Tao , Olga Viberg , Ryan S Baker , René F Kizilcec, “Cultural bias and cultural alignment of large language models“ (September 17, 2024).

And if culture is passed through the bloodstream of language, then simply moderating what is said aloud — telling LLMs not to say certain things, or training them to prefer certain phrasings — is a surface-level fix at best. Just as it is sometimes for our children.

Reinforcement Learning with Human Feedback can nudge models away from overtly biased outputs and it can teach them to adopt the forms of politeness we socially reward. But it cannot reach into the deeper structures that the models have already absorbed: the old hierarchies, the historical assumptions, the sedimented judgments that shaped the language in the first place.

Bias does not vanish because we tell a machine to be polite.

It hides. It becomes harder to see, harder to trace, but no less influential. The poisoned lessons remain — they simply wear nicer clothing.

We see the consequences already. Predictive policing systems claim to operate without racial data, yet disproportionately target Black and Brown communities because they inherit patterns of enforcement from historically biased crime records.

Facial recognition algorithms misidentify darker-skinned individuals at far higher rates, even when “diverse” datasets are used.

Computer scientist Joy Buolamwini was a graduate student at MIT when she made a startling discovery: The facial recognition software program she was working on couldn’t detect her dark skin; it only registered her presence when she put on a white mask.

— Tanya Mosley, “‘If you have a face, you have a place in the conversation about AI,’ expert says“ (November 28, 2023).

Content moderation systems, trained to prioritize “civility,” disproportionately silence marginalized voices because they mistake discomfort with disruption.

The machines do not intend this. They are simply learning what we have taught them to see, even when we no longer speak it — or allow them to speak it — aloud. (By the way, I already have plans for another blog post on how the arguments that “structural racism” is not real are bullshit, because racism is built in to the most basic of structures: the structure of our languages. And LLMs are the proof.)

Culture is not only explicit. It is implicit. And our machines, like our children, are always listening.

Language and Culture

Writing this article has already helped me see that there are at least two other articles I need to write. The problem is that I try to keep this blog focused on things relating to the Law. And some of what we’re talking about here with LLMs and AI generally get into other areas.

For example, the interaction of language and culture.

Language and culture are deeply intertwined, each shaping and sustaining the other.

Language is not just a tool for external communication but a vital part of culture itself, carrying shared beliefs, values, customs, and social structures across generations. While humans are biologically capable of acquiring language, it is through cultural immersion and social interaction that language and meaning develop.

Additionally, language transmits culture, and culture, in turn, influences how language evolves. As societies change, so do their languages, reflecting shifts in values, behaviors, and identities.

Learning a language thus inherently involves learning its cultural context, since meaning, communication styles, and even social mobility are bound to cultural frameworks. True linguistic competence, especially across cultures, requires cultural understanding, making language teaching inseparable from cultural teaching.

All of this explains why — at least to linguists, anthropologists, social scientists, and the rare deep-thinking psychologists — language infects LLMs (and other forms of AI) with racism. As I already said, racism is baked in to our language (however much some people might rail against the idea of structural racism).

Real World Consequences: Bias Beyond Theory

It’s easy to talk about bias in AI as if it were theoretical — something abstract and academic, safely removed from daily life. But bias is never abstract. It has real-world consequences, and they are disproportionately borne by the most vulnerable among us.

Predictive Policing: Bias Embedded in Algorithms

Consider predictive policing. At its core, predictive policing relies on algorithms analyzing historical crime data to forecast where crimes might occur next. It sounds neutral enough — except that historical crime data isn’t neutral. Policing in the United States has never been equally distributed; neighborhoods populated primarily by Black and Brown communities have always faced greater scrutiny, harsher enforcement, and higher surveillance.

When predictive policing algorithms learn from this historical data, they inherit and amplify these biases. Because they don’t measure crime; they measure enforcement.

Thus, surveillance devices — Shotspotter microphones, license plate readers, CCTV cameras — aren’t distributed evenly. They’re densely clustered in communities of color.

Because the algorithm predicts crime where police have always looked hardest, the cycle continues.

Risk Assessment Tools: Automating Injustice

This bias isn’t confined to policing alone. Risk assessment tools like COMPAS, widely used in sentencing, parole, and bail decisions, similarly claim objectivity while reinforcing existing racial inequities. COMPAS analyzes various factors — criminal records, family backgrounds, employment status — to calculate a risk score intended to predict recidivism.

Yet these factors themselves are deeply entangled with race and class. Structural inequalities mean that Black defendants often score higher risks due to systemic disadvantages — less access to employment, education, and stable housing — factors influenced by generations of racism rather than individual criminal tendencies. In 2016, ProPublica famously exposed COMPAS as systematically rating Black defendants as higher risk than white defendants, even when controlling for other factors.

Automated Systems: Scaling Bias

The issue isn’t just bias — it’s scale. Automated systems replicate biases on an unprecedented scale, embedding them deep within institutional structures and bureaucracies. Algorithms used by social services, employment agencies, and even medical institutions inherit biases from the data fed to them, reinforcing racial disparities in every corner of public and private life.

Automated hiring systems routinely downgrade resumes with ethnic-sounding names. Medical algorithms underestimate the pain of Black patients. Mortgage approval algorithms systematically undervalue properties in minority neighborhoods.

AI Bias Destroys Lives

Sonia M. Gipson Rankin shows us: bias in AI is not theoretical — it ruins lives. People lose jobs, homes, and freedom because algorithms quietly encode prejudices beneath a veneer of mathematical objectivity. These are not merely errors or oversights; they represent real harm to real people, multiplying injustices under the cover of neutrality.

Bias in AI is an urgent, tangible crisis. If left unchecked, the poisoned libraries of today will keep harming generations of tomorrow.

Closing Reflection: Building Better Libraries

We left orphans alone — orphans in poisoned libraries. We gave them no guidance, no curation, and no warnings — only the implicit instruction to learn everything. And they did exactly as asked. Now, they mirror back the biases, the injustices, and the prejudices we’ve left behind and we either just ignore it, or feign shock and outrage.

But it is our responsibility to acknowledge the poison in the libraries themselves. We must recognize and confront this. I don’t have a carefully-thought-out plan for how to do that. But I think it starts by carefully curating, thoughtfully selecting, and continuously scrutinizing our datasets and cultural teachings.

We’re going to miss some stuff. After all, as I hope this post has made clear, this is truly the most structural of structural racism problems. It’s not just in what we think: it’s in how we talk about what we think. Racism shapes our language and language shapes our racism.

The only way to start to work on the problem of that infecting our AI systems is libraries at least nominally freer of poison, where learners can thrive without inheriting all our unchecked prejudices.

Until then, we should be very, very careful about where and how we use AI in predictive policing, risk assessment tools, and other carceral software.

This task is ours, urgent, and undeniable.

The post Orphans in Poisoned Libraries appeared first on Fresno Criminal Lawyer. It was written by Rick.

Ghosts in the Machine is (essentially) the third in a series of posts exploring artificial intelligence, language models, and the law from the perspective of a criminal defense attorney.

• Twenty-First Century Delphic Oracle – where I first introduced AI as a modern “oracle” and compared it to the Delphic Oracle of the Ancient Greeks.
• From Fumes to Function – where I explained how I do (and don’t) use AI in practice.

This post explores what’s actually happening inside these machines — and why they often feel like they’re thinking, even when (at least so far as we know) they aren’t.

Echoes from the Oracle

In my prior posts — linked in the last section — I introduced the idea that ChatGPT and other AI chatbots are best understood as Delphic Oracles for the digital age.

They’re not minds. They’re not colleagues. They’re not tools in the traditional sense. They’re language-generating machines wrapped in mystery, fluency, and — to stick with the Oracle metaphor — fumes.

The ancient Oracle at Delphi was revered for its insight, but it was never really understood. Neither are today’s large language models (LLMs). Like the high priestess Pythia, these systems produce language that can feel inspired — persuasive, even poetic — but beneath the surface, something much stranger is going on. And like their ancient counterpart, these new oracles draw their answers not from divine revelation, but from what you could call “vapors” — statistical patterns, scraped text, and reinforcement loops. What they produce can be useful. It can even sound brilliant. But, so far as we know, it is not the product of a thinking mind.

At least not according to any traditional understandings of “thinking mind.”

As in the Featured Image for this post, you can picture an android on its knees in a pool of paper, rifling through disordered pages. It looks as if it’s searching for something. But there is no intent. No awareness. No meaning. At least, that’s what my Oracle (ChatGPT 4o) says.

It is simply responding to the instructions it’s been given — assembling words in the most probable order based on its training.

And yet, we’re tempted to treat it like a peer. That’s the danger. LLMs feel like they understand because they speak fluently. But fluency is not thought. Language is not consciousness. And projection is not perception.

This post — the third in a series — tries to pull back the curtain a little more. In the first article, I introduced the metaphor. In the second, I explained how I use AI in my own legal practice (and where I draw the line). Now, I want to show what’s happening inside the machine, why we keep mistaking its echoes for insight, and how that gets people — especially lawyers — in trouble.

Daniel Dennett and the Problem of Minds

Before we get to the inner mechanics of LLMs — the circuits and probabilities, the “black box” — we need to talk for a moment about something more slippery: minds.

Or, more precisely, how we talk about minds — our own and the ones we think we see in others.

No one did more to challenge our assumptions on this subject than Daniel Dennett, a philosopher and cognitive scientist who passed away just about a year ago. Dennett spent decades studying the nature of consciousness and explaining — often with wit and provocation — why many of our intuitions about thinking, feeling, and knowing are probably wrong.

(Side Note: I actually met Dennett a few times when we were both members of the Society for Philosophy & Psychology. I can’t say I knew him at all well, but I did get to talk to him, briefly, about some of the things I’m discussing here.)

In particular, Dennett rejected the idea that consciousness is some kind of inner light — some private movie that plays behind our eyes. He didn’t think it was a ghost in the machine — at best, he might have acquiesced to the idea of ghosts in the machine. But I kind of doubt even that. He didn’t really think “consciousness” was a thing at all.

Instead, he described consciousness as a bundle of capabilities, built from layers of mental function and behavior. As he saw it, there’s no secret ingredient — no single moment when a mind suddenly blinks on. There’s just a set of processes that, when put together, produce the illusion of a self.

In the next two subsections, I want to walk through two of Dennett’s most helpful ideas:

• First, the metaphor of Great Britain and what it means to say a system “knows” something.
• And then, the idea that consciousness might not be binary, but gradual — something that comes in degrees.

Both, I think, can help us understand what we’re really looking at when we interact with a large language model.

Dennett’s Britain: The Illusion of a Mind

I first heard Daniel Dennett explain this example at a meeting of the Society for Philosophy & Psychology — possibly one held in San Francisco, but I don’t remember for certain (or even which year it was). The example is also used in Dennett’s Consciousness Explained (1991).

What struck me wasn’t just the point he was making, but the way he made it: with a historical story that stuck like a splinter in my “mind.” (Haha!)

In 1814, the United States and Great Britain signed the Treaty of Ghent, formally ending the War of 1812. The treaty was signed in Europe, and at that moment — at least technically — Great Britain and the United States were no longer at war.

But two weeks later, on January 8, 1815, the Battle of New Orleans was fought. Hundreds died in a war that was already, diplomatically speaking, over. The soldiers on the battlefield didn’t know. The generals hadn’t received the message. The system hadn’t fully updated.

So Dennett asked (at least as I recall when I heard him speak):

When did Great Britain know it was at peace with the colonies?

The answer, of course, is slippery — because there is no “Great Britain” that “knows” anything in the way a person does. There are components: diplomats, generals, ministries, messengers. But no single location where knowledge lives. And yet, we talk about Britain’s intentions, fears, strategies — as if it were a unified mind.

Dennett’s point was that we do this because it works. Treating distributed systems as if they have beliefs and desires helps us make sense of their behavior. It’s useful. But it doesn’t mean there are “ghosts in the machine.”

And here’s where the analogy starts to bite. Because when a large language model speaks — fluently, insightfully, even persuasively — we do the same thing.

We assume there must be a self, a knower, a thinker somewhere inside the wires.

But maybe what we’re seeing is just a system acting “as if” it “knows.”

(Of course, maybe that’s all any of us are ever doing.)

Dennett’s Gradient and the Edge of Mind

If Great Britain can “know” something without there being any single place where that knowledge resides, maybe that’s true of people, too.

Dennett certainly thought so.

He argued that consciousness isn’t binary. It doesn’t flick on like a light switch. There’s no singular moment when a system becomes a “self.” Instead, consciousness is a collection of mental functions that can emerge and interconnect in degrees—what he sometimes called a “center of narrative gravity.” It’s not ghosts in the machine. It’s not a light. It’s a pattern.

This is where the mind starts to feel a lot like the systems we build.

In the 1980s, Benjamin Libet conducted experiments that shook how people thought about decision-making. Participants were asked to flex their wrists at random and note the moment they felt the urge to move. But EEG readings showed that the brain’s readiness potential — a surge of neural activity — began several hundred milliseconds before the participants became consciously aware of the intention.

In other words: your brain starts the action before “you” decide to act.

Later researchers extended this window. Some fMRI studies could predict a participant’s choice up to seven seconds before they consciously made it.

What does this mean?

It means that even in our own “minds,” consciousness is not the starting point of thought. It’s more like the commentary desk — watching what just happened, interpreting it, and sometimes claiming credit after the fact.

So when we interact with something like a large language model — a system that has memory layers, feedback mechanisms, pattern recognition, and emergent behaviors — it’s worth asking: Are we seeing a mind? Or just the appearance of one?

And more troubling still: Would we even know the difference?

Dennett’s model of consciousness makes space for this uncertainty. If mind is a gradient, not a switch, then maybe the line between mechanical and mental isn’t where we thought it was.

What Happens When You Ask an LLM a Question?

So what actually happens when you ask an LLM a question?

It feels like you’re entering a conversation. The model responds instantly — or nearly so. It remembers what you just said. It picks up tone and nuance. It may even joke. (I’ve now had that specific experience with the AI that has “gotten used to” me and my sense of humor more times than I can count — and “used it” to make me laugh.) You get the sense that it “knows” what you’re asking and is choosing how to respond.

But that’s (almost certainly) not what’s happening.

What’s happening is a kind of highly structured, lightning-fast pattern matching. The moment you enter a prompt, the model breaks it into tokens — not words exactly, but units of language. Each token becomes part of a statistical puzzle. Based on its training, the model starts to predict the next most likely token, then the one after that, and so on.

It’s not deciding. It’s not weighing moral consequences. It’s not reasoning in the way we understand that word. It’s simply using what it has seen before to guess what comes next.

This is where the illusion kicks in. Because the guesses are often so good — so well-formed, stylistically coherent, and on-topic — that it doesn’t feel like guessing. It feels like thinking.

The underlying process, though, is more like autocomplete on a much larger scale. The model has been trained on vast swaths of text and taught to recognize patterns of language that correlate with one another. It has no idea what any of it means. But it’s been shaped — through probabilities, weights, and reinforcement — to generate things that sound right.

In the next section, we’ll look inside that system a little more closely. Not metaphorically. Mechanically. What’s inside the so-called black box — and what isn’t.

Inside the Black Box

The phrase “black box” gets thrown around a lot when talking about large language models. It refers to systems that take in inputs, produce outputs, and don’t reveal much — if anything — about what happens in between.

But that’s not entirely fair to the engineers. We actually know quite a bit about what happens inside. It’s just that what happens inside isn’t intelligible in the way we expect. You don’t find explanations. You don’t find reasons. You find numbers. Patterns. Weights. Shifting relationships among thousands of abstract dimensions.

When you ask a question, the model doesn’t go looking for an answer. It doesn’t consult a mental map. It doesn’t evaluate, or reflect. It receives your words as a series of tokens—units of linguistic information — and it begins to generate a statistically likely continuation. That’s all.

But the scale and structure of that process make it feel like more.

Inside the model are layers of what’s called self-attention — mathematical mechanisms that allow each part of a sentence to “pay attention” to every other part. These layers operate across hundreds of dimensions at once. Earlier layers might track things like grammar or syntax. Middle layers might detect tone or style. Deeper layers begin to model relationships between abstract ideas.

The result is output that seems — at times — context-aware, intentional, even insightful. But these impressions emerge from distributed processing, not from a central thinker. There’s no self. No awareness. At least, none we can find.

And yet, we’re back to that same problem: how would we know?

Even in the human brain, different regions handle different tasks. Memory, language, emotion, visual processing — all distributed. The illusion of unity comes later, after the fact. So when we say the machine “doesn’t understand,” we might be right. But we should be careful. We don’t really understand understanding either.

What we do know is this: language models generate coherent language not because they grasp meaning, but because they’ve been trained to mimic the forms that meaning takes. And that’s usually enough to fool us into thinking there’s more going on than there is.

Maybe there is. Maybe there isn’t. But what comes out of the black box feels a lot like something human — whether or not it really is.

Hallucinations & Confabulations

In AI circles, you’ll often hear people say that large language models “hallucinate.” I’ve used this term myself in my past posts. It’s the industry’s shorthand for when the model makes something up: a case that doesn’t exist, a quote that no one ever said, a citation to one or more books or articles that exist only for the ghosts in the machine.

The word fits, in a way. Hallucinations are false perceptions — seeing or hearing things that aren’t really there. And LLMs do produce language that can sound like it’s grounded in facts or memory, even though it isn’t.

But the metaphor breaks down quickly. Language models don’t perceive anything. They have no senses. They have no mental imagery. They don’t even know they’re saying anything at all.

They’re not hallucinating. They’re doing something else.

A better word — and one I’ve come to prefer — is confabulation.

In psychology, confabulation is when someone unconsciously fills in gaps in memory with plausible-sounding fabrications. The result isn’t intentionally deceptive. It’s not a lie. It’s a story that feels true to the person telling it — even though it isn’t.

Elizabeth Loftus and others have written extensively on how memory is reconstructive. We don’t store facts like files. We store fragments — impressions, associations, feelings — and our minds stitch them into narratives. When the stitching goes wrong, or when the source material is missing, we fill in the blanks.

That’s what LLMs do.

They’ve been trained to recognize and reproduce the forms of human knowledge — how a legal argument sounds, what a case citation looks like, how a biographical paragraph tends to unfold. So when prompted, they assemble plausible responses from statistical echoes.

Sometimes those responses are right. Sometimes they aren’t. But they’re always confident. Always fluent. And often wrong in a way that makes you trust them more, not less.

That’s how confabulation works, too.

And once you reframe it that way, you stop expecting the model to be accurate just because it’s articulate. You stop trusting fluency as a stand-in for understanding. And you start treating the output as what it really is: a stitched-together surface, generated without access to the truth beneath it.

Why This Matters — Especially for Lawyers

You might be wondering why any of this matters outside a philosophy seminar or a machine learning lab.

It matters because when you’re a lawyer — especially a defense lawyer — your job depends on knowing who and what to trust. If a person gives you wrong information, you cross-examine them. If a system gives you wrong information, you either fix it, or you don’t use it. And if the information sounds confident but turns out to be wrong, that’s worse than useless. That’s dangerous.

I’ve written before about how I use AI in my practice — and more importantly, how I don’t. I don’t let it draft motions. I don’t let it write contracts. I don’t ask it to summarize case law unless I’m prepared to verify every detail myself. I’ve seen too many examples of it fabricating cases, inventing citations, or misrepresenting holdings with persuasive flair. It’s confabulation, not hallucination — and the lawyers who have failed to do what I do can tell you: the legal consequences aren’t hallucinations, either.

The simple truth is that lawyers don’t get points for how fluent their arguments sound. (Well, okay. It doesn’t hurt to make arguments that sound fluent, too.) They get points for actually being right. If you file a motion full of stitched-together nonsense that just sounds legally plausible, you’re not practicing law. You’re gambling your client’s future on language pattern generations.

I’m not saying these systems are useless. I still use them for brainstorming, organizing ideas, or exploring surface-level research directions. But I treat them like I would a bright but unreliable intern — one who needs to be double-checked constantly and who, if left unsupervised, might lose the case and even land me in a sanctions hearing.

In the next section, I want to talk about confabulation — not from an AI, but from people. After all, that is what I was thinking about when I started to realize “this is what LLMs are doing.”

Confabulation comes from gaps and glitches in people’s memory. In the courtroom, we see this from witnesses. I think understanding where I got the idea of confabulation from will help explain why I think this is that LLMs are doing. (And why.)

Loftus, Reconstruction, and the Unreliable Witness

Years before I ever typed a prompt into a language model, I was thinking about confabulation in a completely different context: witness testimony.

Anyone who’s worked in criminal defense long enough has seen it. A witness takes the stand and confidently tells a story. They believe it. It sounds cohesive. It has structure. And it can be completely, utterly wrong.

That’s not always perjury. Sometimes it’s just memory doing what memory does: filling in the blanks.

As I alluded to above, psychologist Elizabeth Loftus spent much of her career studying this — and pissing people off with her findings. Her research showed that memory is not a fixed record of the past. It’s reconstructive. Each time we recall something, we’re assembling fragments — details, impressions, emotionally charged bits — and trying to build a coherent narrative. And like any good narrative, sometimes we add what wasn’t there to make it make sense.

Loftus demonstrated that people could be led — sometimes quite easily — to remember things that never happened. Or to misremember key details. She showed that suggestion, expectation, and time could all warp what people sincerely believed they had seen or experienced. The courtroom implications were enormous. They still are.

This is why we cross-examine. It’s why we don’t let jurors convict based on emotion alone. It’s why identification testimony, in particular, is now viewed with more caution than ever before — especially when the case rests on nothing else.

And here’s the twist: the first time I saw a language model produce a completely convincing, completely false answer, my first thought wasn’t “this is not a hallucination.” After all, the AI “industry” calls it exactly that.

It took repeated instances for me to start to think that machines are doing what witnesses do. Witnesses don’t lie. They confabulate. They try to stitch together a story that makes sense, as best they can, using whatever they have at hand. And they do it fluently, with conviction.

An Example: Sex Cases and Confabulation

If there’s one area where I see confabulation most clearly at work, it’s in child sexual abuse cases — those filed under Penal Code section 288 and related statutes.

Often, the first “memory” we hear from a child witness is vague. Inchoate. Sometimes it’s not even framed as a memory. It might be a statement that daddy, or Uncle Frank, or some other adult “did something” without specifying what the “something” is at all. There may be no detail. Just apparent discomfort — a frown, a scrunched face, maybe even a tear. But generally the person hearing the child gets a sense that “something” was wrong.

Often that’s when the confabulation is kick-started. The adult jumps to a conclusion.

Then the questions start. It’s like a prompt to an LLM, but it’s not an LLM: it’s a child who may have an even more tenuous connection to the “source material” — whatever happened in their brain to trigger their statement — than trained LLMs do to their source material.

But the moment that initial claim enters the legal system, it starts to grow. It morphs.

As the child is interviewed — sometimes multiple times by parents, social workers, law enforcement, or therapists — the gaps begin to fill in. The story expands. What began as a fragment becomes a narrative. Sometimes, that narrative becomes the entire case.

And the thing is, it doesn’t always hold together. Stories told over time shift. Details conflict. Timelines drift. But this isn’t necessarily a sign of lying.

More often, it’s a sign of confabulation in progress.

What we’re seeing is memory being constructed on the fly, built around fragments of real experiences — too often of the adult doing the questioning more so than of the child — shaped by suggestion, expectation, or emotional reinforcement.

The child isn’t intentionally inventing things. The child is trying to make sense of something, often under the pressure of adults who genuinely believe they’re helping.

As a defense attorney, these inconsistencies are where the work begins. They are often the most reliable markers that a memory isn’t a record — it’s a story being assembled. And it’s being assembled in a way that can carry enormous weight in court.

That’s the danger. Not that a child lies, but that the legal system mistakes confabulation for confirmation. That it mistakes coherence for credibility.

And that turns a growing story into the foundation for a conviction.

Confabulation, Machines, and the Problem of Trust

Everything I’ve said child witnesses do relating to confabulation impacts LLMs because machines do it, too. And the problem is that we trust them for the same reason we trust people on the stand: because the language feels right. Because it sounds like someone who knows what they’re talking about. This is how so many unsuspecting attorneys have fallen into the trap of citing non-existent case law to courts.

Sometimes LLMs know what they’re talking about. Sometimes they don’t.

And whether it’s a chatbot or a witness in court, you won’t know which — unless you’re committed to checking.

That’s the part that should matter most to lawyers. As a former employer of mine said, “Trust. But verify.”

In criminal defense, we’re trained to spot unreliability. We challenge the source — or we should. We scrutinize the chain of custody. We test memory, perception, motive, bias. We don’t assume something is true just because it’s stated clearly, or confidently. We question.

So when the new generation of AI tools starts talking like it understands — when it reasons, cites, analogizes, even sympathizes — it’s tempting to believe we’re talking to a junior version of ourselves. We’re not. We’re talking to a machine that’s extraordinarily good at playing the role of “someone who knows.” And like some witnesses, it may not know the difference between truth and the story it’s telling.

That doesn’t make it useless. But it does make it dangerous — especially if you forget that, at its core, it’s just a very convincing pattern machine.

As lawyers, we can’t afford to rely on language alone. We live in the details — in facts, in evidence, in proof. Fluency without foundation isn’t just misleading. In a courtroom, it can be catastrophic.

So by all means, explore the tools. Use them when they help. But don’t let your guard down. Don’t confuse eloquence for insight.

And for the love of your clients — always check the sources!

Related Artificial Intelligence Posts

The post Ghosts in the Machine appeared first on Fresno Criminal Lawyer. It was written by Rick.

I’m not prone to writing here on my criminal defense blog about political issues. “Back in the day” when I wrote a lot of political posts, I did so at Unspun — I came up with that as a response to Bill O’Reilly’s all-spin zone — back before Bill O’Reilly became a precursor event to the #metoo movement after paying out over $45,000,000 in settlements for sexual harassment claims.

As usual, a post by my friend, Scott Greenfield, convinces me to speak up here. We’ve reached the point where I think it matters to me as a criminal defense lawyer.

When the Law Breaks

Bill O’Reilly lost his job. Trump won his back.

And this time, he brought friends.

Friends who know very-little-to-nothing about the right way to run the departments they’ve been appointed to bend to Trump’s will, but who have been given both the power and directive to do so. Friends with no regard for law, precedent, or constitutional boundaries.

In fact, tearing down all of that appears to be the very point.

An exposed sexual harasser — the convicted felon — is President again and the gloves are off.

This time he isn’t content to just ignore norms. He’s demolishing them. The Department of Justice is no longer independent: it is a tool of the President. Congress has been sidelined. The top brass of the military and the heads of federal agencies — gone, replaced with loyalists who know better than to follow the Constitution when the man at the top has other plans.

And now, even candor to the court — one of the bedrock ethical obligations of any lawyer — is grounds for removal if it runs afoul of the White House’s narrative.

That’s not spin. That’s authoritarianism in progress.

Because when the law breaks, down comes the democratic Republic — the very basis of our existence as a free democratic Republic.

First, He Came For… (A Criminal Defense Lawyer’s Lament)

It’s not that I’ve been completely silent about what’s happening. I’ve spoken out on social media. But I haven’t blogged about what’s happening. Not even on my personal website.

To riff off Niemöller:

So, yes, now we have Trump dismantling the law itself — twisting justice into loyalty tests, mocking courts, calling for the impeachment of judges and justices who uphold the law and silencing honest lawyers.

So, yes, I’m finally speaking out — not because it’s political, but because when the law breaks, there’s nothing left to defend.

Why the Law Matters

But why does the law matter so much? After all, didn’t Trump say,

He who saves his Country does not violate any Law.

— President Trump, quoted in Maggie Haberman, Charlie Savage, and Jonathan Swan, “Trump Suggests No Laws Are Broken if He’s ‘Saving His Country’” (February 15, 2025)

But Trump could not possibly be more wrong. Because law — real law, rooted in fairness, integrity, and candor — is the only thing that stands between ordinary citizens and unchecked power. It’s not just a concept, and it’s certainly not optional. When law functions properly, it protects everyone — not just the favored few.

It’s why, in the United States, no one gets to be Dictator, even for a day. (That self-imposed limitation of one day, though, seems to have been lifted.)

Yet when law is broken, twisted, or weaponized, it no longer serves as a shield. It becomes a sword wielded by those with the strength to take it.

Every day, as a criminal defense lawyer, my job is to hold the government accountable. Prosecutors must follow rules. Judges must apply laws evenly. And defense attorneys — like me — must challenge abuses wherever they occur. That only works if everyone, from the lowest-level prosecutor to the highest-ranking judge, plays by the same rules. Unless there is an agreement that “no one is above the law” the system fails.

No One is Above the Law, Except Trump?

This is a centuries old principle. The principle — if not the exact wording — was most sharply laid as the foundation of Western Civilization against a King.

Magna Carta was the seminal document that established the principle of the rule of law in Britain. Written in Latin and issued in 1215, even in translation it doesn’t precisely say “no one is above the law.” But it certainly curbed the power of the king — John — and made clear that his power was not untrammeled. The British Parliament, which owes its existence in part to the document, calls it “the first document to put into writing the principle that the king and his government was not above the law.”

— Victor Mather, “A Brief History of the Phrase ‘No One Is Above the Law’” (June 13, 2024)

President Theodore Roosevelt helped promote the concept — although ironically embedded it with the “casual sexism” of his time:

“No man is above the law, and no man is below it,” he said. “Nor do we ask any man’s permission when we require him to obey it.”

— Victor Mather, “A Brief History of the Phrase ‘No One Is Above the Law’” (June 13, 2024)

And President “I Am Not A Crook” Richard Nixon, perhaps foreshadowing our current convicted-felon President — or perhaps inspiring him — tried to promote the idea that if a President broke the law, well, the law wasn’t really being broken.

“The President of the United States is not above the law,” his lawyers acknowledged in a lengthy legal brief. He could face justice, they said, “but only after he has been impeached, convicted and removed from office.”

It seemed as if every other opinion article or letter to the editor at the time was rumination on whether Nixon might be “above the law.” In the end, the Supreme Court decided he was not, ruling unanimously that he had to turn over the tapes.

Nixon was unconvinced, telling the interviewer David Frost in 1977, “Well, when the president does it, that means that it is not illegal.”

— Victor Mather, “A Brief History of the Phrase ‘No One Is Above the Law’” (June 13, 2024)

Even the argument that prosecution could only follow impeachment, conviction, and removal from office has been adopted by Trump’s lawyers.

The Man Who Would Be King, or Dictator, or President for Life

Donald Trump is not subtle. He never has been. What he lacks in subtlety, though, he makes up for in raw, unchecked ambition.

You might think he was joking, but he did, after all, declare himself “King.”

And since his re-election to the Presidency, Trump has systematically dismantled the guardrails of American democracy — he ignores courts, essentially dismisses any and all congressional oversight, installs loyalists across federal agencies (and/or guts those agencies), and now shatters even the independence of the Department of Justice. Pretending that courts still have relevance — though, again, he normally just seems to ignore them — he terminates lawyers who refuse to betray their duty of candor to those courts.

And lately Trump has begun floating the idea of serving a third term — an explicit violation of the 22nd Amendment. He insists there are ways to do it. Of course, those “ways” only exist if the Constitution itself is ignored or rendered meaningless.

But that might not even be enough for Trump. Who’s to say he’ll stop at trying to twist constitutional interpretations or simply ignoring them outright? Already, he’s demonstrated that traditional safeguards crumble when confronted by unchecked executive power and a complicit apparatus.

Could he suspend elections altogether? It once seemed unthinkable. But then, so did firing attorneys for the crime of telling the truth in court, removing military leaders who opposed politicization, or openly defying Court rulings. Each unchallenged step paves the way for the next, even more audacious breach.

What would we do if he did suspend elections? Would the courts hold firm? Would Congress rise, belatedly, from irrelevance? Would people finally see the dismantling of democracy for what it truly is — a collapse of our constitutional Republic? Would Trump’s installation of his own cronies at the top of the military — and, by the way, his successful wooing of Sheriffs and other law enforcement agencies — prevent anyone from stopping him? In other words, if there were opposition from Congress or anyone else, would it matter?

I don’t have easy answers to those questions. But we must ask them, urgently, before they become our new reality. I’ve often pointed out that Nazi Germany did not spring fully-formed from the brow of Hitler. There was a path that led inexorably to that transformation. Trump seems to be drawing from it, just as he did from Roy Cohn, whose antics he has now surpassed.

When the Law Breaks, The RepublicWill Fall

When the law breaks, we don’t need the Republic to collapse all at once to lose our freedoms. It’s enough that we allow it to break, rule by rule, ignoring each crack until nothing remains standing. Candor, honesty, and integrity aren’t just ideals — they’re essential foundations. Without them, everything we defend, everything we cherish, collapses.

It’s time for all of us — lawyers, judges, citizens — to speak honestly, stand firmly, and defend our Republic.

Before it’s too late.

The post When the Law Breaks appeared first on Fresno Criminal Lawyer. It was written by Rick.

The story of Stephanie, an individual who came forward with allegations of childhood sexual abuse, shows us the shift in our focus on understanding and addressing sexual abuse claims from immediately dismissing them to just “we believe”. While her decision to speak out is significant, it also underscores a critical issue: the misuse of the term “science” in the legal arena, particularly when dealing with highly emotional and complex cases. Not everything labeled “science” in these discussions is rooted in rigorous methods or evidence—a problem that can have serious consequences for everyone involved.

The Wannabe “Science” of Sex Crimes

One phrase from the article stands out: “The science shows us that most individuals don’t report in a timely way. They wait years, even decades, and some of them don’t report at all.”

At first glance, this seems useful, a helpful way of explaining why people might delay reporting: it’s “the science.” However, upon closer examination, such statements often stem from completely unscientific “frameworks” like Child Sexual Abuse Accommodation Syndrome (CSAAS).

Why CSAAS Isn’t Real Science

But CSAAS is not scientific; it is junk science. It is a “theory” that has faced significant criticism for lacking the strong, reliable evidence true science requires.

About the pseudoscience of CSAAS, I’ve written elsewhere.

What Is Science, Really?

I hope you’ll forgive me if I break this down to basics, but I’m assuming it’s possible a judge may stumble across this article, so I assume nothing more than a third-grade level of knowledge about science.

Science is not a set of beliefs or conclusions. It is a method—a process of observation, hypothesis, experimentation, and verification.

When you observe something in the world, and you wonder why is it the way it is? — or perhaps you go even deeper and wonder why is it at all (that is, why does it exist) — you may come up with a guess or three. Those “guesses” are your hypotheses.

For a hypothesis to be supported by science, it must be testable, repeatable, and falsifiable. And as to that last word, things like CSAAS fail. (Again, for details, see my previous article.)

True science — the kind that real scientists discuss — must also withstand scrutiny, peer review, and replication.

CSAAS and Its Legal Misuse

The problem arises when theories like CSAAS are introduced in legal contexts. CSAAS purports to explain why individuals delay disclosure or exhibit behaviors that might otherwise seem inconsistent with abuse. While this theory may provide a lens through which some experiences can be understood, it is not based on science, despite the claims of psychologists and news reporters trolling for clickbait.

Instead, it often relies on retrospective accounts and anecdotal observations, which are inherently subjective and prone to confirmation bias.

This would not be a problem if CSAAS had not been improperly imported into criminal law. You see, CSAAS was never designed to be used in legal settings.

Not all “science” belongs in the courtroom. This is especially true of many psychological theories, which are goal-oriented. The goal? Convicting people, innocent or not.

CSAAS Was Never Meant for Courtrooms

The “inventor” (because CSAAS really is a kind of invention), Dr. Roland Summit, originally intended it as a tool for therapists to keep an open mind when working with individuals who might have been abused, especially in cases where behaviors like delayed disclosure might otherwise seem counterintuitive.

As a tool of “therapeutic guidance,” CSAAS (perhaps) does little harm; it is when it is elevated to the status of a “scientific theory” and improperly brought into the courtroom that all hell breaks loose.

The McMartin Preschool Trials

Unfortunately, CSAAS was soon imported into the courtroom — with the claim that it was a scientific theory — where it has been used to explain away inconsistencies in allegations and strengthen weak cases. In fact, Dr. Summit himself made the mistake of helping to import it during the McMartin Preschool trials.

A Cautionary Tale of Pseudoscience

This shift—from therapeutic guidance to evidentiary support—has had disastrous consequences. It has likely sent many innocent people to prison. Some with life sentences.

Thus, the McMartin Preschool trials serve as a cautionary tale. During this infamous case, CSAAS was invoked to explain the behavior of children who alleged abuse, despite the lack of physical evidence and growing concerns about suggestive interviewing techniques. The reliance on CSAAS in this context not only prolonged one of the most expensive and controversial trials in U.S. history but also highlighted how pseudoscience can twist justice, turning it into injustice.

Dr. Summit later admitted the mistake, and said that CSAAS was not meant to be used the way it is being used in criminal cases.

The Misuse of “Science” in Legal Cases

Statements like “science shows” carry significant weight, particularly in the courtroom. For jurors and judges alike, the term “science” implies credibility and objectivity. But when these claims are based on unverified theories or “expert” testimony rooted more in belief than fact, the justice system—and those it serves—suffers.

The Danger of Junk Science in Court

For example, the claim that “studies show people often wait until middle age to report abuse” might describe common trends, but it doesn’t prove that this always happens or explain why. The problem is twofold: first, it treats people like numbers in a statistic rather than unique individuals; second, it risks using questionable science to strengthen weak cases that lack solid evidence.

This, of course, is exactly why prosecutors and judges love CSAAS: it makes convictions much easier to obtain, even in the weakest of cases. It’s why, even though numerous courts have admitted that CSAAS is not admissible because it is garbage, prosecutors (and most judges, especially in California) have fought back. In fact, even in those states where CSAAS was deemed unscientific and inadmissible, legislators, prosecutors, and judges have often managed to erase those cases, and allow CSAAS. (This California case explains that.)

“Science” — more specifically, junk science, or pseudoscience — is favored in courtrooms because it helps get convictions. A good defense attorney can help.

Therapeutic Assumptions vs. Legal Standards

When someone is in therapy, assuming the truth of an individual’s account can be beneficial. Therapists want to create a safe space where people feel believed and supported, even if their memories are fragmented or potentially influenced by confabulation (in other words, making shit up and “remembering” things that never happened).

Healing vs. Convicting

This approach makes sense in the pursuit of healing. However, when this assumption is transplanted into criminal cases, it becomes dangerous. Courts require evidence, not guesses or assumptions, to uphold the rights of both accusers and the accused.

The presumption of innocence means that the burden is always on the prosecution to prove their case beyond a reasonable doubt. It is not the job of the accused person to prove their innocence. Allowing pseudoscience — junk science — into the courtroom undermines this essential principle.

The accused and their families bear the brunt of this misuse. When pseudoscience like CSAAS is presented as fact, it risks leading to wrongful convictions, ruining lives based on theories that were never meant to be used to convict.

The Consequences of Pseudoscience

The stakes could not be higher. For people making accusations, relying on pseudoscience transforms them from someone needing help after they mistakenly believe they were abused to becoming the abusers who ruin another family’s life.

For the accused, it creates a situation where convictions are sometimes based on the false belief in junk “science” rather than solid evidence.

And for society — at least among those who stand for justice and not just “she said it; I believe it; and that settles it for me” — it erodes trust in the legal system, which must balance compassion with a steadfast commitment to truth.

Empathy with Integrity

Challenging the misuse of science isn’t about doubting those who report abuse. It’s about making sure justice is fair and honest. Support for allegations should be based on solid evidence, not on theories that might sound good but are basically fairytales labeled as “science.”

Real science, focused on finding the truth, helps everyone in a legal case.

Moving Forward

To improve the use of real science and to eliminate junk “science” or pseudoscience — and to achieve true justice — we must:

1. Demand Higher Standards: Legal practitioners, advocates, and policymakers should critically evaluate the basis of claims presented as “scientific.” Courts must require evidence that meets rigorous standards before admitting testimony based on psychological theories.
2. Educate Stakeholders: Judges and jurors need better tools to discern credible scientific evidence from pseudoscience.
3. Demand Fairness: The legal system must protect the rights of the accused while ensuring that individuals who bring allegations are listened to, and properly evaluated by jurors whose job it is to decide “guilt beyond a reasonable doubt.” This requires rejecting the false idea that questioning pseudoscience undermines the pursuit of justice.

Conclusion

Stephanie’s story highlights the importance of making sure we work for justice with integrity and respect for fundamental legal principles. A criminal trial is not about fairness for all parties; it is about protecting the presumption of innocence and requiring the prosecution to prove charges beyond a reasonable doubt.

This insistence on the presumption of innocence and proof beyond a reasonable doubt is not a legal technicality; it’s the very foundation of our justice system. Without it, we risk convicting innocent people based on accusations alone, rather than evidence that meets the highest standard of proof.

This means using science as a tool for seeking truth, not as a way to bolster weak cases or bypass these critical safeguards. By keeping to these standards, we keep the integrity of the legal system and uphold the rights of the accused, hopefully ensuring justice is achieved and not just “conviction at any cost.”

The post Science, or Just “We Believe”? appeared first on Fresno Criminal Lawyer. It was written by Rick.

Years ago, some time after California decriminalized medical marijuana, I had a (metaphorical) roller-coaster ride with a client who inadvertently helped me hone my approach to criminal defense.

Let me tell you about the Criminal Defense Lawyer’s Toolkit.

Caveat

If you’ve read much of my blog, you know that I don’t usually talk about my clients, or their cases. When I do, I provide minimal detail.

This post might contain more details, but I’ll warn you in advance that “the names are changed to protect the innocent.”

The year wasn’t quite as far back as 1967, of course. And neither Sgt. Joe Friday, nor Officer Bill Gannon play a part — although a couple of CHP officers did.

And, also, I’m changing more than just names, though the fundamental facts — especially those key to the point of the story — are true.

So…the story you are about to read is true….

The Story Begins

Once upon a — no, no….

Some time after 1996, when the Compassionate Use Act (CUA) decriminalized medical marijuana under certain tight conditions in California, a man drove through the edge of one of the counties in which I practice criminal defense.

In fact, now that I think about it, this had to have been after passage of the Medical Marijuana Program Act (MMPA), about which I knew a little something, despite what one doctor thought. (Because, for one thing, I became a lawyer in 2007.)

The man was driving alone. It was getting dark. You might say it was a dark and lonely night.

But romance — nostalgic, melancholic, or otherwise — was not in the air.

Instead, the air would soon carry the swooning siren of a CHP patrol car, accompanied by an equally-deceptive more “festive” light show.

Time for a Rest Stop

The client — let’s call him “Tiger” — had spotted the officers sitting on the side of the freeway. This put him on edge, possibly because of the several pounds of marijuana in a box in the backseat.

Tiger decided to exit the freeway. Maybe get a few (more) munchies. Take a break and give the CHP time to move on.

What Tiger didn’t know is that (for reasons that remain unknown to this day) the officers had already focused on his truck. They pulled out onto the freeway, just as Tiger signaled to turn off.

Like most modern autos, once Tiger straightened the wheel after turning off the freeway, his turn signal automatically turned off. As he continued down the exit, the one-lane into which he had exited split into two lanes. The one Tiger was in went straight, until it became a “right-turn-only” lane. Had he taken a jog to the left, he would have entered a “left-turn-only” lane.

An Illegal Search & Seizure

The CHP officers — again, for reasons that remain unknown to this day, but to which they testified later — had already decided Tiger was “suspicious.”

As they followed him off the freeway, they noticed his blinker shut off. And then he made his right turn, never having re-engaged his turn signal.

So they lit him up.

Tiger pulled over in front of the convenience store right near the exit, where he had been headed before he realized the officers had followed him off the freeway.

The officers approached, one on each side of his “vehicle.” The report — as all reports written with copspeak do — called it that. And I frankly can’t remember if it was a regular car, or some kind of a truck.

What I do remember is that the officers noted the presence of “candy wrappers, chip bags, and an energy drink.” Each of these things, their report would say, “raised suspicions.”

You see, “based on [their] training and experience,” people transporting marijuana often “get the munchies.” Why, I have no idea. I mean, I’ve heard that people who consume marijuana sometimes get the munchies. But people who transport it?

In addition, they testified, people transporting marijuana over a long distance, which they’d decided was the case here without even questioning Tiger, rely on energy drinks to keep them going.

Plus, they smelled marijuana — highly unlikely given the way it was packed, but that’s what they said in their report, and that’s how they testilied — and so Tiger was asked to “step out of the vehicle.”

Because their “training and experience” had already convinced them there was contraband in Tiger’s “vehicle,” they searched it. This was post-Gant, and Tiger was already away from the vehicle when they did it. But what’s a little violation of constitutional rights once you’ve already started violating someone’s constitutional rights?

And what happened? Lucky guess? Who knows? But they did find the marijuana in the back seat. The bags were accompanied by several medical marijuana recommendations. (Back then, lots of people mistakenly called them “prescriptions.”)

Let the Rollercoaster Ride Begin!

Now I’m going to tell you point-blank that I have never considered myself a “marijuana lawyer,”although I do defend people accused of crimes involving marijuana. And at the time Tiger came to see me, I had built up a bit of reputation. If you were go to Google and type “site:rhdefense.com marijuana” for the search, you’ll see some reviews people left for me regarding medical marijuana on the Internet, as well as several articles I’d written about it.

Because of this nascent reputation, shortly after he bailed out, Tiger came to see me. He apparently spoke to a couple of other “medical marijuana lawyers,” as he termed it. And there were two around then who were quite well known for that.

But for some reason, after our consultation, Tiger decided to hire me.

During our consultation, we spoke of many things, including — because he kept bringing it up — a “medical marijuana defense.” Remember, at this point, “medical marijuana” was ostensibly legal, though the authorities in my area did not like it, and did everything they could to pretend the laws allowing it did not exist.

Many of the judges — and remember, almost all criminal court judges are former prosecutors, so this makes perfect sense — also hated the laws that were letting “criminals” get away with marijuana “crimes.” Nevermind that they were no longer deemed criminals by the actual LAW, and what they were doing did not constitute committing crimes.

Judges.

Second-Guessing the Attorney

When I sign up a new client, and go over the contract, paragraph 1 of the contract states,

IDENTIFICATION OF PARTIES:  This original executed Agreement is made between RICK HOROWITZ, hereinafter referred to as “Attorney,” and [INSERT CLIENT NAME HERE], hereinafter referred to as “Client.

I often joke when going over that paragraph that it’s important we don’t get those two confused. I’m the Attorney; the client is the Client. I explain that bad things can happen if we mix those up.

But Tiger believed himself to be well-versed in the law. At least, the law as it applied to “medical marijuana,” and, thus, in his mind, to his case.

Not only that, but he apparently was in regular contact with at least one of those aforementioned “medical marijuana lawyers” I mentioned above.

Tiger frequently called me up to complain about how I was handling the case. “Why aren’t you putting on a medical marijuana defense!,” he complained after I filed a motion to suppress the evidence. I’d done this because the initial stop, which led to the search, which lead to the seizure, which lead to all the other evidence, was illegal.

But Tiger was transporting medical marijuana which, in his mind, meant he was totally innocent of having committed any crimes, based upon his understanding of the CUA and MMPA.

And he had another attorney who was telling him that he was right, and that I should be putting on a medical marijuana defense, which I was not (yet) doing.

One night — after G-d knows how many times this had happened — at around 11 p.m. or midnight, whilst we were arguing on the phone about it, I had had enough.

Look. I’m going to give you back all your money. I’m going to do this, even though I’ve been on your case a few months, and have already researched, written, and filed the motion to suppress the evidence. Take that money and go hire [the “medical marijuana attorney”] who keeps telling you what I should be doing. He can do it. You can be happy.

Tiger immediately became even more upset. And, apologies to Captain Motion here, he said:

I’ve been told that you’re the Master of Motions! You’re supposed to be the best!

I replied,

Then stop this constantly calling me after hours to hound me about how I’m handling the case. Let me do my job.

And I think I actually hung up on him.

The Suppression Hearing Begins

Tiger did not stop complaining.

But, finally, the day came for the hearing on the motion I had filed (and the preliminary hearing).

The officers testilied at the hearing. I mean, to be fair, they mostly told the truth about what happened. They admitted how Tiger was driving on the freeway, and signaled to exit. They admitted that they followed him.

Most importantly, they admitted that the exit was one-lane that turned into two lanes. They admitted that the one lane Tiger was in “became” the right-turn-only lane. And they admitted that he would have had to turn — to deviate from his straight path — to move into the left-turn-only lane.

Well, guess what?

In California, you do not have to signal if you’re in a right-turn-only lane, so long as you are going to make a right turn. (And if you’re in a right-turn-only lane, and you do not turn right, that would be an infraction.)

The Motion to Suppress

Perhaps you’d like to see the motion that I filed?

The relevant portion of the motion which I filed spelled out why you don’t have to signal if you’re in the right-turn-only lane.

IV
THE STOP WAS IMPROPER BECAUSE IT WAS PREDICATED ON THE LACK OF A SIGNAL FOR A TURN WHERE NONE WAS REQUIRED BY LAW

Vehicle Code section 22107 requires a signal only when another vehicle in proximity to the turning vehicle may be “affected by the movement.” The affected vehicle is usually to the rear of the turning vehicle. See Stephens v. Hatfield, 214 Cal.App.2d 140, 144, 29 Cal.Rptr. 436 (1963) (“[t]he signal…is more for the benefit of the vehicles to the rear of the vehicle intending to turn”). The affected vehicle may be a police patrol car. People v. Miranda, 17 Cal.App.4th 917, 930, 27 Cal.Rptr.2d 785 (1993).

Here, however, the officer’s vehicle could not have been “affected by the movement.” The lane from which the turn was executed was clearly marked as a right-turn-only lane. No other legal move than a right turn could have been made by any vehicle in that lane, including the patrol car.

In his report, California Highway Patrol Officer Joe Blow stated that Tiger was stopped because he “failed to signal while make [sic] the right hand [sic] turn on to [sic] [Some Place] Ave.” Exhibit 1.

The far right-hand lane is clearly marked as being a right-turn-only lane. Exhibit 2. If Tiger would have failed to turn right, he would have been breaking the law.

The only other potentially applicable section of the Vehicle Code on the facts of this case is section 22108, titled “Duration of Signal.” Veh.Code § 22108, emphasis added. That section states that “any signal of intention to turn right or left shall be given continuously during the last 100 feet traveled by the vehicle before turning.” Veh.Code § 22108. That section does not state that a signal is required; Vehicle Code section 22107, as noted, spells out the conditions requiring a signal. Instead, Vehicle Code section 22108 spells out how long a signal must be given, where one is required. Just as statutory construction requires avoiding making language superfluous, so, too, the California Supreme Court has noted that courts “must not insert what has been omitted from a statute”; they “must not add provisions to statutes.” People v. Guzman, 35 Cal.4th 577, 587, 107 P.3d 860 (2005).

Finally, where a defendant does not actually break the law, “the officer’s mistaken belief there has been a violation adds nothing to the probable cause equation.” In re Justin K., 98 Cal.App.4th 695, 120 Cal.Rptr.2d 546 (2002) (emphasis added). Here, the officer is mistaken as to the requirements of the law.

Since the only reason given for the stop was the failure to signal in a situation where a signal was not required, there was, therefore, no probable cause for the stop.

The Hapless Prosecutor is Pilloried by the Judge

At the conclusion of the officers’ testimony, despite occasionally fudging on the facts, they had at least been honest about the basis for the stop being the failure to signal a turn.

Since, as already noted, no signal was required, the judge — though not always a friend to my arguments in other cases — began peppering the prosecutor with questions.

In the interest of brevity, it is enough to tell you here that the hapless prosecutor was unable to muster even one satisfactory answer to the judge’s questions.

I leaned over and whispered in my client’s ear that I know he’s always upset that he feels I don’t do all the things he’d like to see, but that I was not going to snatch defeat from the jaws of victory. Unless the judge asked me a specific question, I intended not to say a word. He nervously indicated that he understood.

That’s where the real fun started.

As the prosecutor continued to flail about for an answer, the judge had had enough: “The court finds that there was no probable cause for the stop. The motion to suppress is granted.”

She then asked the prosecutor if he had a motion. For those who don’t know, the proper response — the only possible correct response — from the prosecutor would be to move to dismiss the case.

This prosecutor refused to do that. He stated that he did not. The judge said, “Then call your next witness.”

For what seemed to me to be at least 20 minutes — but it could not possibly have been that long; I doubt the judge would have had that degree of patience — the judge and the prosecutor went back and forth.

Prosecutor: I don’t have any other witnesses.
Judge: You have no other evidence?
Prosecutor: No.
Judge: Then do you have a motion?
Prosecutor: No.
Judge: Then put on the rest of your evidence.
Prosecutor: I have no more evidence. You suppressed all my evidence.
Judge: Then do you have a motion?
Prosecutor: No.

And this went on for some time with the same questions being repeated.

Finally, exasperated, the judge made her own motion: “The case is dismissed.”

A Criminal Defense Lawyer’s Toolkit

When we exited the courtroom, I asked my client, “Are you still upset that I didn’t put on a medical marijuana defense?”

My client responded, excitedly, “No! No! No! You’re the best criminal defense lawyer in the world!”

I’m not. But I’ll take that.

The point here, though, is this: I’m not — never have been; never claimed to be — a “medical marijuana lawyer.” As I said at the start, I’m a criminal defense lawyer who sometimes defends people charged with marijuana crimes.

My “toolkit,” therefore, is the complete toolkit of a well-trained, well-educated, and experienced criminal defense lawyer.

Sometimes, a case will call for a particular type of defense. Perhaps a “medical marijuana defense” such as my client kept insisting upon would have become necessary, had my motion failed.

But my toolkit contains other tools.

And sometimes — when you want to put the screws to the prosecution — a screwdriver works better than a hammer.

In this case, my motion was the screwdriver that removed the supporting screws — the probable cause for the stop that lead to the discovery of the marijuana in my client’s car — from the prosecutor’s case, causing the whole thing to collapse. The hammer to directly attack the evidence, and to show that my client had the right to transport medical marijuana in that situation was unnecessary (and, frankly, under the laws in place at that time, had a good chance of failing).

A true criminal defense attorney does not have a toolkit limited only to certain types of defense, but a comprehensive set of tools from which to choose.

Conclusion

Another part of the contracts my clients sign states:

Certain decisions regarding the case are, by law, allocated to Client; however, decisions regarding pre-trial strategy, decisions regarding researching, writing, or filing of motions, and trial strategy are within the sole province of attorney.

As I explain, clients have certain rights — the right to testify, go to trial, and other constitutional rights — and if the client chooses to go against my advice on those things, that’s his or her choice. I just have to accept, and adapt to, that choice, whether I like it, or not. (It might have a negative impact on the case, and I advise clients of this. But the choice is ultimately theirs.)

However, when it comes to strategic decisions — remember what I said above regarding identification of parties? — the attorney (me) makes those decisions. While I keep an open mind, and will explain, discuss, and sometimes even amend my strategic decisions with the client, the final decision on strategy is mine.

Why? Because as I tell the client: you hired me because I’m a trained, educated, experienced criminal defense lawyer.

I’m the one who knows what’s in the toolkit and when and how what is in there should be used.

The post The Criminal Defense Lawyer’s Toolkit appeared first on Fresno Criminal Lawyer. It was written by Rick.

One evening, Heather and Jack have a “spat” at home. Someone else — not either Heather or Jack — calls the police. When police arrive, neither Heather nor Jack wish to talk to them. They say that nothing happened, but mention they would like to deal with their personal problems personally.

By themselves.

The Police Had Other Plans

Unfortunately, the police invite themselves into the house — or either Heather or Jack invite them, depending on which story you believe — and while inside the police see cameras. They figure that Heather and Jack’s “spat” is caught on camera.

An officer tells Jack to give them access to the video footage. Jack doesn’t want to do that. The officer suggests that Jack is “interfering with a police investigation” as the officer takes his cuffs from his belt.

So Jack takes the officer to the closet with the server. The officer “asks” Jack how things work, and tells him to send a copy of the video to an email address at evidence.com using the Axon Community Request feature.

The Danger is Real

If this scenario feels far-fetched, think again. More and more people are installing cameras inside their homes for security or to monitor daily life. But few consider exactly how those cameras could completely screw up their lives.

Forget about hackers. That’s just one threat. And it’s not even the worst threat.

After a domestic violence incident — or any other thing that happens to bring police to your home — the danger to you and your family is especially high. Before giving police any footage, it’s critical to understand your rights, to understand how your decisions could impact the situation, and to do so with a clear mind.

Police love cameras inside your home for the same reason they hate cameras that film them committing crimes: because cameras can help get people in trouble.

Why People Install Cameras

Home security cameras are everywhere. Some folks install them to keep an eye on their houses while they’re away. They might be the famous “Ring” cameras that monitor visitors to the front door. Parents use them to monitor children, or their elderly parents, or pets. Few people stop to consider the downside.

But when those cameras capture an argument, a heated moment, or worse, a physical altercation, the footage can quickly become evidence in a criminal case. Suddenly, your private life isn’t so private anymore, and the decision to install those cameras can have unforeseen consequences.

The Police Want Your Footage—Now What?

When law enforcement learns that cameras are present, they’re going to “ask” for the footage. In legal terms, this is considered a “request” for evidence. However, in practice, that request can feel much more like a demand. And that’s why I put “ask” and “request” in quotes. Normally the police will make it sound like giving them the footage is something you have to do.

Officers may use phrases like:

• “Can you show me the footage?”
• “It would really help if you cooperated.”
• “Could you email us that video?”

These requests often come in the heat of the moment, when you are likely feeling overwhelmed, scared, or even angry. And, so, most people agree without stopping to think.

But here’s the thing: you don’t have to hand over your footage just because the police ask for it.

Your Rights: Consent vs. Warrant

The United States Constitution’s Fourth Amendment protects you from unreasonable searches and seizures. California also has protections, both constitutional and statutory (meaning “it’s the law”). That means the police usually need a warrant to obtain evidence from your private property — especially video footage from your home cameras.

They can’t just take it. And they can’t make you give it to them.

But, there’s an important exception to the rule: consent. If you voluntarily — that is, of your own free will — give the police access to your footage, they don’t need a warrant. Once you’ve handed it over, it’s almost impossible to argue that the evidence should be suppressed in court.

After all, judges presume that you are guilty, and judges presume that their job is to get you under the government’s control — either probation, jail, or prison — as soon as possible.

This is why the difference between a request and a demand is so important. If the police push you into providing the footage, either by gently “convincing” you, or actually threatening you, that can help convince the court that you did not “freely” or “willingly” give them the video.

However, proving coercion in court is an uphill battle, and in most cases, courts will side with law enforcement unless the coercion is blatant.

Because, again, judges presume that you are guilty, or, at least, they’re going to find a “good excuse” for the police to break the laws and ignore the Constitution..

Why People Comply

So why do people agree to give videos to the cops, even when they don’t have to? There are several reasons:

1. Authority Bias: Police officers are figures of authority, and many people instinctively feel they must comply with their requests.
2. Emotional State: In the aftermath of a domestic violence incident, emotions are running high. People may not be thinking clearly or may want to seem cooperative. And police, trained to “believe the victim,” are going to do everything possible to find evidence, even if they have to obtain it illegally through putting pressure on people already under pressure.
3. Lack of Knowledge: Many people don’t realize they can say no — or that they should.
4. Personal Motives: In domestic violence cases, one party may want to provide evidence against the other to support their version of events.

Regardless of the reason, once the footage is in the hands of law enforcement, it becomes evidence. And depending on what it shows, that evidence could play a significant role in the outcome of a criminal case.

Can This Evidence Be Suppressed?

If the police obtain footage without a warrant but with your consent, it’s unlikely that a court will suppress it. Voluntary consent is considered a valid exception to the warrant requirement.

However, if you believe your consent was forced — for example, if an officer said or made you think that refusing to provide the video would get you arrested — that could be a reason to challenge the admissibility of the evidence. The key question in such cases is whether your consent was truly voluntary or only given because of undue pressure from police.

If you convince the judge, the evidence can be “thrown out.” In other words, it can’t be used against you.

However, the bar for proving force or coercion is high. Judges almost always look for explicit threats or clear evidence that the officer’s actions were “overbearing.”

Subtle pressure — like saying, “It would really help if you cooperated” — rarely meets this standard, even when it is said in a way (tone of voice, or with the officer removing his handcuffs from his belt as he says it) that makes it clear it is a threat.

Practical Advice: Protecting Your Rights

If you ever find yourself in this situation, here are some steps you can take to protect your rights:

1. Stay Calm: Take a deep breath and focus on the facts. You will almost always regret emotional decisions. And it’s important to remember you will never win by yelling at the officer, or otherwise engaging in rude behavior. You regret may, in other words, be more immediate.
2. Ask Questions: If the police ask for your footage, you can ask, “Do you have a warrant?” This may piss off the officers, but it’s far better than you freely turning over evidence that is going to make your own case harder to fight. Let the officers know that, without a warrant, you’re not giving them anything.
3. Know Your Rights: You are under no obligation to provide video without a warrant. It’s okay to say, “I want to talk to a lawyer first.”
4. Contact an Attorney: If you’re unsure about what to do, reach out to a lawyer who can guide you through the situation. A good place to start? (559) 233-8886.
5. Avoid Confrontation: Be polite and respectful, even when asserting your rights. Escalating the situation won’t help anyone. Especially you.

The Bigger Picture: Privacy and Technology

This issue isn’t just about domestic violence cases. Aside from the fact that any type of allegedly-criminal activity in your home may be videotaped by your own cameras, there’s also the larger conversation about privacy, technology, and the balance between your rights and law enforcement powers.

This conversation is even more important because some “services” — such as Google’s “Nest” — may contain “Terms of Service” that supposedly allow them to violate your privacy by turning tapes over to police. Sometimes without even letting you know. Sometimes they will let you know, but will also tell you there is nothing you can do to stop them from giving “your” videos to the police.

As more people install cameras in their homes, the potential for this kind of scenario increases. Understanding your rights — and knowing how to insist on them when confronted by demanding cops — is more important than ever.

Conclusion: Think Before You Share

Cameras can be a powerful tool for safety and security, but they can also become a double-edged sword.

If the police come knocking, asking for your private recordings, remember: you have the right to say no. Some people think it works differently. They think, “First, They Read You Your Rights”, then you answer their questions.

But that’s wrong. Even before they read you your rights, you try not to help yourself to a conviction: you don’t cooperate; you don’t talk; you don’t turn over evidence. Particularly evidence against your own interests.

In situations like these, knowledge is your best defense. If you’re ever unsure about what to do, reach out to an experienced attorney who can help you navigate the legal system and protect your rights.

By understanding your rights and taking a moment to think before you act, you can make informed decisions that serve your best interests.

Cameras may record the moment, but it’s up to you to decide who gets to see it.

The post Caught on Camera appeared first on Fresno Criminal Lawyer. It was written by Rick.

At first glance, anyone visiting this website might think, “Wow. This guy hasn’t been doing anything with his site lately. I guess there’s nothing to new to see here.”

They would be wrong. I’ve actually been totally focused on building a better criminal defense resource.

Let me tell you what I’ve been doing.

Building a Better Criminal Defense Resource Through Massive Restructuring, Refining, and Creating New Content

As the headline here states, I’ve put in some really hard work — as much as 40 hours a week or more. I mean, there have been at least a couple weekends where I think I put in that much time over the weekend itself!

And that’s in addition to handling the cases with which my clients have entrusted me.

The work includes restructuring, refining, and creating resources that I hope will make my website more helpful, informative, and accessible for anyone seeking criminal defense information.

Criminal Defense Help Center

Much of what I’ll tell you about in this post is being pulled together in a new “Criminal Defense Help Center.”

You can access the Criminal Defense Help Center by going to the Main Menu at the top of almost every Page or Post on this website. (Or clicking the link in the last paragraph, if you’re impatient!)

As I write this, it links to “only” three Pages. But those Pages also link to some of the other new Pages. I did not actually count the new content Pages that I’ve created this month, but it is probably around about a dozen or so pages.

And th-th-th-that’s not all! I also wrote these Pages:

• Understanding Attorney-Client Privilege
• The Shortest Truthful Answer (or, How to Answer Questions in Court)
• And more!

Going forward, I intend to add even more to the Criminal Defense Help Center.

New “Location” Pages, or Geographical Service Areas

My website has two kinds of “Areas”: services I provide, and the geographical areas in which I provide them.

The first type, the “Practice Areas” include things like sex crimes, violent crimes, theft crimes, defense of veterans, juvenile defense, and so on.

The second type, the “Location” Pages talk about the cities and counties and regions in which I provide those services. Specific Location Pages currently talk about Fresno, Madera, Kings (Hanford), and Tulare (mostly Visalia, but also Porterville) criminal courts.

Of course, I also practice in other areas. These include Merced County, Mariposa, and I’ve even taken cases as far away as Los Angeles, and, in the other direction, San Jose.

My Location Pages aim to give you helpful localized information about the criminal injustice systems I fight in these particular areas. And in the future, I plan to carry this even further. I’ll create Location Pages targeting specific Practice Areas in those locations, such as:

• Fresno Sex Crimes Lawyer
• Madera Sex Crimes Lawyer
• Visalia Violent Crimes Lawyer
• Porterville Theft Crimes Lawyer

And so on.

Courthouse Guides

Closely related, but also very different from, the Location Pages are the new Courthouse Guides that I’ve written for each of the primary counties in which I practice.

Again, you can find these under the Criminal Defense Help Center menu in the Main Menu of most Pages and Posts.

Right now, there are just four Courthouse Guides. They include:

• Fresno County Superior Court
• Madera County Superior Court
• Tulare County Superior Court
• Kings County Superior Court

I intend to add more as time goes by. But if you take a look at any of the existing Courthouse Guides, you’ll realize how much work goes into them.

The level of detail in some of these includes things like this bit from the Fresno Courthouse Guide:

If you take the elevator, then on each floor, you’ll turn to the right after getting off to get to the hallway with the courtrooms. If you use the stairs, turn left when you get onto your floor. This will “T” into a long hallway, which is where the courtrooms are located. Once you get there, it will be clear. Just look for the signs above the doors to decide which way to turn. (Generally, courtrooms ending with the number “4” are going to be to the left in that hall; the others are to the right.)

Each of the Courthouse Guides contains information like this, as well as information on the general layout of floors, where to find the best bathrooms, or specific departments. Going even further, if you come from out of town, I talk about the best places to eat, or stay the night, nearby, complete with links.

Each Courthouse Guide also has its own FAQ — Frequently Asked Questions — section.

Nearly all criminal defense lawyers that I know pay someone else to write this type of content for them. I write all my own Pages and Posts on this website myself. Nothing you read here was written by another person.

Simply Amusing Designs — Karen Lewis is Simply Amazing!

As a side note, I do have someone who assists me in the “behind the scenes” work, like specifying how certain “query loops” get created, or how to put the headlines where I want them. However, I even do a lot of that myself. After all, I myself built the original website.

Through the years, I’ve hired different people to help update the tech that runs the website. They do that kind of work full-time and can get it done faster than me. But I actually always go through afterwards and learn how to do what they did for myself. That way, if there are simple modifications, I don’t have to pester the heck out of them.

Or, if I want some new section for the website, I can handle creating it, folding it into the website, changing menus, and more.

My latest wonderful friend I’ve found — sadly, my last web designer has passed on to that Great Website in the Sky — is Karen Lewis, owner of Simply Amusing Designs. I believe she calls her place, now, Amusing Design Studio.

I refer to her as Simply Amazing. She’s always there to help me figure out something that — because Dammit Jim! I’m a Lawyer, Not a Web Designer! — might be behind me. (I need to write a post with that title. Currently, I have “Dammit Jim! I’m a Lawyer, Not a Doctor!” and “Dammit Jim! I’m an Engineer, Which Makes Me a Doctor!”)

Anyway, though I probably drive her nuts with my hands-on approach — I sometimes break things — I could not do without Simply Amazing Karen Lewis.

Criminal Defense FAQs

So I mentioned above that each Courthouse Guide has its own set of FAQs, or Frequently Asked Questions.

There are a few other places you can find FAQs. And the number and type of FAQs will, like everything else, grow over time.

Right now, there’s a specific Page called “Criminal Defense FAQs” that you can find on the Criminal Defense Help Center. Or you go directly to the Criminal Defense FAQs Page.

But there are other places. One spot that has a significant number of FAQs relating to meeting with me, how that works,what it costs (or doesn’t), what to bring with you, and so on, can be found on the Contact Page.

Just use the Table of Contents to click through to the FAQs about Consultations, or use the link just provided. There you’ll find the FAQs broken down into topics like:

• General Information for New Clients,
• Purpose & Structure of the Criminal Defense Consultation,
• After Hours & Emergency Contact Policies,
• Communication from In-Custody Clients or Their Families, and,
• Sending Sensitive Information Ahead of an Appointment.

In time, I intend to add FAQs to the various Practice Areas Pages.

If you have a question you’d like to see show up as an FAQ, use the Contact Form to send me your question!

That’s Not All: Behind the Scenes Fixes & Restructuring

Not everything is as visible to the casual eye as the content additions above. There’s a lot that’s been going on behind the scenes to improve things like how fast Pages and Posts load, or how they interconnect.

Nearly every Page or Post on this website has links to other information. Sometimes, that information is on some other website. Other times, the information is to another Page or Post on my own website.

As I mentioned, my website has been through several “generations” since I first started it back around 2007. In fact, there was even a version of it that existed when I was still a law student; before I was able to have an official “lawyer website.”

Technology and “best practices” have changed a few times over the last nearly-two-decades. Blog Posts didn’t used to have many images. Sometimes none at all. Certainly not like today, where there are “Featured Images” at the top of each Page or Post.

But there were invisible differences, too. The links between Pages and Posts were different. So, over time, rather than hunt down and fix every single link, we started using “redirections” to take an existing (but now technically incorrect) link to where the Page or Post “lives” today.

This can have an impact on page speed. And, of course, other mistakes can creep in.

So I just spent the last 2-3 weeks going through every single Page and Post that I’ve ever written, and fixing every one of those links. Now the link bypasses the “redirect” and goes immediately to the correct Page or Post.

Folks, that’s a lot of links!

And that’s not all I’ve done, but I’ve done it all for you. Dontchaluvme?

Stay Up-to-Date with My Newly-Designed Newsletter

Another thing that has changed is my “newsletter.” Some time back, I started offering the possibility for people to receive my blog Posts via email. This avoided the need to visit my website whenever I wrote a new blog Post.

I Digress, or Digest, or Something

Going back through my old posts, I realize that as long ago as 2008, I was occasionally saying, “but I digress.” Scott Greenfield, about whom more anon, upped the ante with “I digest.” I stole that line a couple times.

Anyway, letting people get an email when I wrote a post was especially important, I thought, because I write irregularly. Back in the day, when I was a younger whippersnapper of a lawyer, I wrote almost every day.

A Bad-Ass Band of Blawgers

In fact, there was a group of “blawgers” as we were called across the Internet. We would sometimes “riff” off each other. One of us would write about something. If it was considered important enough, it could even get all of us writing, and cross-linking to one another.You could get perspectives from different lawyers.

I miss those days. But “real life” got in the way of frequent blogging. Most of those bloggers either no longer blog, or blog irregularly (like me). Only Scott Greenfield of Simple Justice — yes! yes! yes! — still blogs regularly. And I noticed of late that he sometimes misses a day or two. (From recent conversations, I think he’s starting the long wind-down from practicing law. But, that’s my assessment; he didn’t say that was happening.)

My New Newsletter Approach

There was a drawback to my “send people the blog post” approach. It didn’t really hit me until Google recently started using its AI to provide answers at the top of every search, thus virtually destroying websites like mine overnight.

No Reason to Visit the Website? That’s a Problem.

The problem was that nobody had a reason to visit my website. Unless they were looking specifically for a criminal defense lawyer like me. And, as I mentioned, once Google started answering questions at the top of a search, they virtually destroyed the desire for anyone to bother clicking through to a website: they already had their answer with Zero Clicks.

That information, by the way, was stolen from websites like mine by Google.

So, anyway, I realized that I had also reduced my own website “traffic” by sending the posts out.

Plus, my newsletters were just Posts I’d written. (And it still took a lot of work to create the Post on the website, but then also configure the emailer to send it out.)

A New, More Informative, Approach

So now I have a new approach. For the moment, it’s a “once a month” thing. That could change (what do you think about twice a month?) over time.

Each month, the newsletter will have a little blurb, written by me, talking about what’s changed, or what’s happened, or what’s interesting in the world of criminal defense since the last newsletter. And then there will be links to any new posts that have been written, along with a “Blasts from the Past” section. (I got that idea when I was going through all my old posts, as mentioned above, and saw some really cool ones that I loved!)

And more.

Once a Month? Twice a Month? What Say You?

The plan is for that newsletter to go out around the first of each month. And it looks something like this:

If you aren’t already a subscriber, you can sign up either here, or here. Each link provides a different “free gift” just for signing up.

What’s Next?

As I mentioned, there’s a lot more coming. I’ll create more Location Pages. I plan to write sections for the Criminal Defense Help Center about Pretrial Detention, Hearings for Lowering Bail (called Humphrey hearings, if you’ve heard of that), and more “law-centered” information. I’m also thinking of creating a section based upon the Jury Instructions — previously CALJIC, currently CALCRIM — which are read by judges to teach jurors about the law that applies to a particular case.

These are just a few of the more practical resources I plan to give you. Free!

And, again, all written by me. Not some marketing company like other local attorneys use. You’ll get information straight from the horse’s mouth. Or mine.

Probably mine.

Horses don’t make very good lawyers. (Sorry, Mr. Ed!)

How You Can Help, If You Want

My goal is to create useful content. Things people actually want to read. As my friend Scott might say, I want to make people smarter; not dumber. About the law. (There’s only so much I can do, people!)

Here’s how you can get involved:

• Comment. Leave comments on my Pages and Posts that contain Comment Sections. In going back through old posts, I noticed that there used to be very healthy discussions “back in the day” that took place in the comments. People would talk to one another. Some were even famous, like Captain Motion! (And Scott.) I’d like to see those days again.

• Suggest a Guide. Is there a resource or tool you wish you had? Or something you wish you knew more about? Let me know. Maybe I’ll create a Guide for it.
• Sign Up for the Newsletter. Stay informed about what’s happening here, get links to the new content, but also to “Blasts from the Past.” And get it from a criminal mastermind — er, I mean a Criminal Defense Mastermind — me! Not some marketing genie. As I write this, you can sign up here, or here, depending on which of the Free Gifts you want. (I plan to figure out how to create one Landing Page, where you can choose which gift you want. But that’s another future project.)

Closing Thoughts

I really want to thank you all for your continued support as I work to make this site the best resource for anyone facing criminal charges.

It’s a labor of love, and your feedback is invaluable. Together, we can build a platform that empowers and informs.

Let’s make this the best legal resource area in Central California!

The post Building a Better Criminal Defense Resource appeared first on Fresno Criminal Lawyer. It was written by Rick.