• Privacy and surveillance.
• Censorship.
• Antitrust, general economic regulation, and other competition management.

Here’s some of what I think about that, plus links to a lot more.

1. For a long time, I’ve maintained:

• Privacy and surveillance are very big deals.
• Ultimately, they cannot be handled effectively without direct regulation of specific permitted and forbidden uses of data.

The first point is now widely accepted. The second unfortunately is not; laws and regulations generally state who may or may not record, keep or decrypt particular kinds of data, rather than what particular uses they may make of it.

2. Another threat to freedom has arisen as big as that from privacy/surveillance: a many-fronts push for censorship. It would ultimately be calamitous for free countries to agree that the threat of “Fake News” and other dangerous online partisanship justifies general censorship, by governments or “platform” tech companies as the case may be, yet that is exactly the path we seem to be on.

Fortunately, there are less dangerous ways to address the same challenges. I expect to make as much fuss about this issue in the upcoming decade as I have about privacy/surveillance over the past one.

3. There are increasingly many calls to break up large internet companies, under existing antitrust laws or perhaps new ones. There is some precedent for actual breakup of technology companies, mainly the 1982 breakup of AT&T/the Bell System in telephony and a couple of rounds of divestiture by the GE/RCA/NBC broadcasting/electronics companies in 1932 and 1942. More important, perhaps, have been less-than-breakup agreements to promote or allow competition, such as Bell’s 1956 agreement to license its patents for free, IBM’s 1956 agreement to compete somewhat fairly in professional services, IBM’s further agreement in 1969 to “completely” unbundle hardware, software and services, and Microsoft’s tolerance of independent web browsers.

As for some particular, recently suggested competition-protecting ideas:

• I agree with people like Elizabeth Warren that Facebook should never have been allowed to buy Instagram or WhatsApp, because those services are all different aspects of substantially the same business. And I’d support a forced divestiture now.
• I think Google has been a pretty responsible oligopolist, with little harmful overlap between its businesses, and doesn’t need to be broken up. But that also suggests a forced breakup, e.g. separating search from Android, wouldn’t do much harm.
• Amazon Web Services and Amazon’s online store are two quite separate businesses. Forcing a formal separation wouldn’t have many important consequences, good or bad.
• Here’s one where I differ strongly from Elizabeth Warren et al.: The idea of prohibiting a retailer from having “house branded” items is ridiculous. Supermarkets surely make over 100% of their profits from prepared foods that are pretty much all house branded. Companies that sell their own products online shouldn’t be prohibited from selling auxiliary third-party products as well. Amazon is no exception to this general rule.
• Finally, I have long favored a middle course on network neutrality. But if the only politically practical possibilities are the two obvious extremes, then I favor enforced neutrality by far over “any bias goes”.

And whatever happens otherwise in competition enforcement, I’d support antitrust exceptions for certain technology research and study — certainly in multiple security-related efforts, and perhaps around language understanding as well.

4. One interesting note is how commonly tech-related policy issues turn out to be non-/bi-partisan.

• Privacy/surveillance issues commonly are so, most extremely in the case of the Email Privacy Act, which passed the House of Representatives 419-0 despite being so controversial it actually never got through the Senate.
• Everybody’s mad at social media companies these days.
• The populist/anti-elitist aspect of pressure on tech companies seemed mainly like a right-wing/Steve Bannon thing … and then the backlash against New York’s proposed giveaway to Amazon happened. That was mainly from the left. Ditto, I imagine, for the San Francisco and Seattle residents who oppose gentrification.

Of course, there are exceptions, for example:

• Democrats generally support network neutrality and Republicans generally don’t.
• The Trump Administration’s opposition to immigration sometimes extends to tech workers and to students who may some day become such.

5. And finally, government procurement of technology has been a costly mess for many decades, worldwide, occasional improvements such as those during the Clinton Administration in the United States notwithstanding. And the cost is not just in money; with better knowledge management technology, the FBI might have connected dots to prevent the 9/11 attacks.

Private-sector large-enterprise technology acquisition is no picnic either, but it’s a lot better than government’s. Government contracting procedures have got to be changed.

Related links

• I summarized many of the political threats to the tech industry in December, 2017.
• My posts directly focused on public policy in the area of privacy/surveillance include:
  • Very chilling effects (July, 2013).
  • What our legislators should do about privacy (and aren’t) (July, 2013).
  • Where the privacy discussion needs to head (March, 2012).
  • Essential questions of fair data use (July, 2010).
• Some of what I wrote about technology-related economic development in May, 2006 still holds true.
• I’ve long been on the skeptical side about technology patents.

• “Fake news” and the like are severe problems.
• Algorithmic solutions have not worked well to date.
• Neither have manual ones.
• Trusting governments to censor is a bad idea.
• In light of the previous points, trusting large social media corporations to censor is a bad idea too.
• Educating consumers to evaluate news and opinions accurately would be … difficult.

And further:

• Whatever you think of the job traditional journalistic organizations previously did as news arbiters, they can’t do it as well anymore, for a variety of economic, structural and societal reasons.

But despite all those difficulties, I also believe that a good solution to news/opinion filtering is feasible; it just can’t be as simple as everybody would like.

1. When people think about these problems, they’re probably most focused on social media platforms such as Facebook, YouTube or Twitter. But before getting to those, let’s consider the simpler case of search engines. In essence, what search engines do is:

• Assign a relevance score to the relationship between a site (or particular page) and your query.
• Assign a quality score to a site.
• Combine those two scores into an overall ranking, and serve up results accordingly.

How well does this work? I’d say that search engines:

• Are good at directing you to information that is generally related to what you want to know. This is the technological core of what they do.
• Are good at shielding you from the worst cheating/spammer/hacker sites. That’s also a major technical focus for them.
• Are poor but not horrible at distinguishing between good and bad sources of information, opinion or advice. Generally, they do this via some kind of popularity contest, whether via Google’s venerable PageRank or by more directly observing which sites users seem to go to and stay at.
• Don’t even try to filter sites according to leanings such as political bias.

Lessons from that start:

• Huge technology companies can actually do pretty well at the parts of the problem that technology alone can solve.
• A lot of the challenge boils down to adversarial information retrieval, where the adversaries range from somewhat honest polemicists or hucksters to completely awful hackers and spammers.

2. When defending against bad actors, scale helps a lot. In my favorite example:

• A significant fraction of all the world’s email goes through Gmail.
• Thus, it is very hard for email spam blasts to escape Google’s honeypots.
• Informed by those honeypots, Google does what in my opinion is a very good job of fighting spam.

Similarly, as the publisher of multiple blogs, I can tell you that much the same is true of WordPress’ Akismet’s fight against spam comments. Akismet isn’t perfect; indeed, I’ve stopped adding new content to the blog where this post would fit best — Text Technologies – because of a multi-year spam attack. But on the whole Akismet works very well.

Thus, in contradiction to many observers, I believe that the huge scale of social media companies is NOT the root of the problem.

3. Of course, concern is really focused on social media, and especially on the concern that people communicate things they (supposedly) shouldn’t, where:

• “Communicating” includes words, pictures, videos, etc.
• “Shouldn’t” covers outright lies, great factual distortions, hate speech … or just opinions that the would-be censor doesn’t think should be spread.

And even if you don’t worry so much about those problems, some kind of censorship, filtering or gatekeeping is inevitable anyway, simply because there’s vastly more information in the world than any one person can consume.

So what are the main options for censorship and other gatekeeping? My opinions start:

• Having governments be in charge of censorship is a terrible idea.
• Having large, non-journalist corporations be directly in charge of censorship is also a bad idea, because ultimately they’ll just succumb to government or other political pressure.
• The traditional modern gatekeepers are journalistic organizations, who both deserved and received trust that they’d do the job responsibly. But that model no longer suffices in its old form, for several sets of reasons:
  • New requirements. Traditional journalistic gatekeeping boils down to organizations vetting the content they themselves produce. It doesn’t work nearly as well for third-party content, worthy efforts such as fact-checking columns notwithstanding.
  • Trust. Walter Cronkite is long dead; journalists aren’t nearly as trusted as they used to be.
  • Deliberate bias. Opinion and bias are now part of many “news” organizations’ business models, to a much greater extent than they were a few decades in the past.
  • Money. Most journalistic organizations have much slimmer news budgets than they had at their peak.

4. So if we need gatekeeping, and no natural kind of gatekeeper can on its own be effective or safe, what’s left? In simplest terms, we need gatekeeping by (technological) committee. Mainly, what I propose comprises:

• Different kinds of gatekeeper for different aspects of the problem, including at a minimum:
  • Human-led filters to deal with various issues in credibility and bias.
  • Technology-led filters to deal with pure fakery and false provenance.
  • Further filtering of the kinds that would be needed even in a more benign world.
• Multiple choices for at least the human-led filters.
• Good, simple (!) user interfaces for combining those filters’ results.

Above all, people must be able to choose their own censors.

5. What I envision for the “human-led filters to deal with various issues in credibility and bias” is something like:

• An organization maintains slowly-changing whitelists and blacklists of information sources.
• The same organization fact-checks or other vets specific stories, claims and content in near-real-time.
• The results scale to other stories, claims and content via very-rapidly-retrained machine learning models, whether those are based on a single gatekeeper’s hand editing or, more likely, on multiple hand-edited training sets and other collaborative inputs at once.

Here an “organization” can be anything trusted by enough people to be economically viable, for example:

• An offshoot of an existing journalistic organization.
• An offshoot of an existing political party or advocacy organization.
• A successfully started-up new entity.

Obviously, there would be business issues, notably:

• Costs. Who pays, in an economy where news is commonly “free”?
• Chicken-egg adoption. Which gets developed first: Human-led filtering services that can’t yet be integrated into actual social media filtering, or technology to integrate human-led filtering services that don’t yet exist?

But given the importance and visibility of the problem, optimism about solving the business issues is appropriate. The hardest part is the technology itself. Can machine learning models be retrained on a sub-hour or even sub-minute basis? Sure. That’s been confirmed many times. But what I’m suggesting is a pretty complex case, with global scale, intermediate results passed among organizations, with plenty of adversarial elements, all done at very high speed.

That is not yet a solved problem. But it certainly seems solvable. Further, it’s a problem that must be solved, lest liberal democracy be as doomed as some people fear it actually is.

Related links

• I wrote a bit about adversarial analytics in May, 2016.
• I outlined my views about the “War(s) on Truth” in February, 2018.
• Earlier this month, Cory Doctorow offered a hard-hitting column on the dangers of expecting internet companies to do our censorship for us.
• More sedately but with more explanation, so did Will Oremus.

The recent Supreme Court case Carpenter v. United States is a big deal. Let me start by saying:

• Most fundamentally, the Carpenter decision was based on and implicitly reaffirms the Katz test.* This is good.
• The Carpenter decision undermines the third-party doctrine.** This is great. Strict adherence to the third-party doctrine would eventually have given the government unlimited rights of Orwellian surveillance.
• The Carpenter decision suggests the Court has adopted an equilibrium-adjustment approach to Fourth Amendment jurisprudence.
  • The “equilibrium” being maintained here is the balance between governmental rights to intrude on privacy and citizens’ rights not to be intruded on.
  • e., equilibrium-adjustment is a commitment to maintaining approximately the same level of liberty (with respect to surveillance) we’ve had all along.
  • I got the equilibrium-adjustment point from Eugene Volokh’s excellent overview of the Carpenter decision.
    

*The Katz test basically says that that an individual’s right to privacy is whatever society regards as a reasonable expectation of privacy at that time.

**The third-party doctrine basically says that any information of yours given voluntarily to a third party isn’t private. This includes transactional information such as purchases or telephone call detail records (CDRs)

Key specifics include:

• The actual issue in Carpenter is whether the government needs a warrant to access cell phone location data that allows a cell phone user’s movements to be quite accurately tracked. The decision on that issue was Yes.
• This was a 5-4 decision. Chief Justice Roberts and the four liberal justices voted for it. Swing voter Justice Kennedy and the other three conservative justices voted against it.
• The majority was united. Justice Roberts wrote the decision and there were no concurrences.
• The dissents were not united, but did generally focus on two kinds of arguments:
  • Reliance on the third-party doctrine, commonly expressed as reliance on the Smith and Miller precedents.
  • Disagreement with the Katz test.

Also very relevant was the 2012 case requiring warrants for GPS tracking, United States v Jones. But discussion of the Jones decision is confusing, because while some justices at the time addressed the issue of general electronic tracking of a person’s movements, others focused narrowly on the physical action of installing the GPS device.

Unfortunately, all the good news in Carpenter notwithstanding, the decision doesn’t come close to accomplishing as much as we need. I stand by my oft-repeated observations:

• Massive surveillance is inevitable.
• Unless the uses of the resulting information are VERY limited, freedoms will be chilled into oblivion.

Justice Roberts correctly wrote in the Carpenter decision:

Mapping a cell phone’s location … provides an intimate window into a person’s life, revealing not only his particular movements, but through them his “familial, political, professional, religious, and sexual associations.”

Justice Kennedy however rejoindered:

What persons purchase and to whom they talk might disclose how much money they make; the political and religious organizations to which they donate; whether they have visited a psychiatrist, plastic surgeon, abortion clinic, or AIDS treatment center; whether they go to gay bars or straight ones; and who are their closest friends and family members.

His point, also correct, was that the data that police are allowed to get without warrants is even more privacy-violating than the data Carpenter keeps away from them. And so, as good as the Carpenter decision apparently is, privacy invasion and surveillance are still among the gravest threats to liberty that we face.

Related links

• My January post on the chaotic politics of privacy is relevant both to the content of the Carpenter decision and to the fact that the decision-makers did not split perfectly along traditional partisan lines.
• My 2013 series on privacy theory suggests that gaps in judicial reasoning could be filled by referencing the problem of chilling effects.

• Build something brittle.
• Strengthen it over time.

In many engineering scenarios, a fuller description could be:

• Design something that works in the base cases.
• Anticipate edge cases and sources of error, and design for them too.
• Implement the design.
• Discover which edge cases and error sources you failed to consider.
• Improve your product to handle them too.
• Repeat as needed.

So it’s necesseary to understand what is or isn’t likely to go wrong. Unfortunately, that need isn’t always met. 

Murphy’s Law and exaggerated fears

We should always bear in mind Murphy’s Law, which in its simplest form states: Anything that can go wrong, will. But also remember that Murphy’s Law is a joke; and even if it were serious, nothing concise is ever precise.

People who tend to over-believe in Murphy’s Law include but are hardly limited to:

• Bureaucrats.
• Worried parents, especially of only children. (Later kids tend to have it easier, as their parents have more experience.)
• Any buyer or voter you believe has been over-persuaded toward fear, uncertainty and doubt.
• Relational bigots who view the Ted Codd guarantee as an absolute requirement for data management.

Adversaries

The strongest scenarios for Murphy’s Law should be adversarial ones, in which somebody is actively trying to cause problems. But even there it doesn’t always apply. For example:

• Information security commonly fits the Murphy model. Hackers keep outwitting defenders.
• Email spam, however, does not. It’s pretty much of a solved problem; the few spam emails that still get through hardly matter.
• Web search is somewhere in between. Both sides are partially successful in the combat over adversarial information retrieval, as “good” and “bad” sites alike are both well-represented in search results.

Single-impetus failures

Since bad or scary things will happen — Murphy’s Law isn’t entirely wrong — a standard design practice is to avoid single points of failure. Brittleness has a lot to do with which single points of failure have been overlooked; improvement has a lot to do with belatedly cleaning them up. In adversarial scenarios, avoiding single points of failure relates closely to defense in depth.

Some of the nastiest surprises occur when failures have no obvious single point, yet wind up being possible from a single impetus.* This happens when multiple points or moments of failure are somehow correlated, or when they actually cascade. Examples vary widely, including:

• The collapse of the World Trade Center buildings.
• An authoritarian leader who manages to destroy a whole democratic system of government.

IT examples that are relatively big deals include:

• Security breaches in which an attacker becomes able to fully impersonate a well-credentialed user.
• Power outages or other whole-building breakdowns that bring down all parts of a (locally) redundant cluster.
• Software bugs that bring down all parts of a supposedly redundant system at once.
• Analytic failures that stem from misleading data sets. (Garbage in, garbage out.)

*I chose the phrase “single impetus” rather than “single cause” because NOTHING has a truly single cause; things only can happen when all kinds of conditions are satisfied for them to succeed. But there can indeed be an identifiable force, plan or occurrence that sets a chain of events in motion, and that’s what I’m calling the “impetus”.

Related link

• A lot of analytics turns out to be adversarial.

*It’s also common to use the word “harden(ed)”. But I think that’s a poor choice, as brittle things are often also hard.

0. As a general rule in IT:

• New technologies and products are brittle.
• They are strengthened incrementally over time.

There are many categories of IT strengthening. Two of the broadest are:

• Bug-fixing.
• Bottleneck Whack-A-Mole.

1. One of my more popular posts stated:

Developing a good DBMS requires 5-7 years and tens of millions of dollars.

The reasons I gave all spoke to brittleness/strengthening, most obviously in:

Those minor edge cases in which your Version 1 product works poorly aren’t minor after all.

Similar things are true for other kinds of “platform software” or distributed systems.

2. The UI brittleness/improvement story starts similarly: 

• Graphical user interfaces can present users’ choices clearly, making them great antidotes to users’ initial lack of system knowledge or training.
• Usability testing and engineering can lead to improvements and the removal of glitches.

Unfortunately, however, as systems add or change features, UI navigation can get more difficult over time rather than easier.

In at least one scenario — plane crashes due to confused-pilot error — the consequences can be literally fatal.

3. Sometimes brittleness just doesn’t get solved.

• Security is perhaps the most visible example. Almost every security system can be broken, and bad actors actively do so.
• Another example was 1980s-90s CASE (Computer-Aided Software Engineering), specifically in the area of generating code from specifications. The technology was only able to generate apps that performed a limited set of functions — too limited to be useful outside of certain niches — and never successfully evolved further.

4. Large organizations are riddled with screw-ups. One of the most successful large enterprises in world history was the US military of World War 2 — and that is literally the organization where the word snafu was coined.

The response is often bureaucracy. Somebody makes a mistake; procedures and rules are then instituted to ensure the mistake is never repeated. Over time, many rules and procedures build up, until organizational systems are hardened. Business processes wind up taking many steps, each of which represents both a cost and a potential for failure. And sometimes the only decisions that successfully get through the process are uninspired, uncreative or flat-out wrong.

This is the classic example of “hardening” — commonly expressed via its rough synonym “calcification” — adding even more brittleness than it removes.

Outward-facing/regulatory bureaucracies can be even worse..

• Regulators generally have two constituencies — consumers/general public and businesses — with the benefits of regulation going to one and the costs going to the other. Anything regulators do will likely displease at least one constituency.
• These ever-unsatisfactory regulations can often only be changed through a long administrative or legislative process.
• Violating regulations has unpredictable but sometimes severe consequences.

The whole thing is a colossal mess.

5. Many of the previous points apply to enterprise applications, which facilitate business processes, have UIs, and commonly involve platform-like technology as well.

Changing enterprise apps can take both discontinuous and incremental forms; you can either replace your old apps outright or change something in (or in the implementation of) the ones you have.

• If you rip-and-replace your apps, you’re likely to also do so to your business processes, and vice-versa. Discontinuous business process change is often seen as a great virtue, sometimes under the buzzname business process reengineering (BPR).
• If you want to change your processes more incrementally, you likely need one or both of two things:
  • App software with more features than you initially need. That may be easy to get, but it isn’t cheap.
  • A nimble IT department. That one is neither cheap nor easy.

6. My biggest reason for writing about brittleness and improvement is to approach some topics around analytics and AI. As previously noted:

• Artificial intelligence is facing public skepticism both for being too accurate (!) and not accurate enough.
• Analytics in general is often surprisingly inaccurate.

Please stay tuned.

Related link(s)

• Most of what I wrote in my December, 2015 series about artificial intelligence still holds true.

1. Privacy regulations will be very different in different countries or regions. For starters:

• This is one case in which the European Union’s bureaucracy is working pretty well. It’s making rules for the whole region, and they aren’t totally crazy ones.
• Things are more chaotic in the English-speaking democracies.
• Authoritarian regimes are enacting anti-privacy rules.

All of these rules are subject to change based on:

• Genuine technological change.
• Changes in politicians’ or the public’s perceptions.

And so I believe: For any multinational organization that handles customer data, privacy/security requirements are likely to change constantly. Technology decisions need to reflect that reality.

2. Data sovereignty/geo-compliance is a big deal. In fact, this is one area where the EU and authoritarian countries such as Russia formally agree. Each wants its citizens’ data to be stored locally, so as to ensure adherence to local privacy rules.

For raw, granular data, that’s a straightforward — even if annoying — requirement to meet. But things get murkier for data that is aggregated or otherwise derived.

3. Data anonymization needs to be credibly and reliably solved. Reliable data anonymization, in principle, could moot multiple conflicts, including:

• The tension between data sovereignty and global analytics.
• The tension between medical research and health data privacy.

But doing so will be extremely hard.

• Consumer internet deanonymization is brutally effective.
  • Birth date/zip code combinations come close to identifying people uniquely.
  • So does the computer/browser configuration information that gets communicated to any website.
• Ordinary citizens are only beginning to realize how not-anonymous they really are.
• Mistrust of data-rich technology companies has recently exploded.

4. Transparency will be demanded, in multiple forms. For starters:

• The complexity and sophistication of data privacy regulation will naturally lead to demands that compliance be straightforwardly auditable.
• Those demands will come from regulators and consumers alike.

We more or less know how to do that part already.

But where things get really messy is in the area of “black box” algorithms. People are concerned about potentially-untrustworthy automated decisions in many areas, such as:

• Self-driving cars (which occasionally kill people).
• Junk-filled news feeds (which make democracy more difficult).
• Various “creepy” e-commerce behaviors.

In principle, I stand by my opinion from 2012: When consumers lose trust in algorithmic decision makers, it can be at least partially regained by a shift to translucent modeling. But the mathematics of accomplishing that seem — as it were — rather unclear.

Related link

• Already in 2011, denanonymization and other technologies of privacy threats were very effective.

So let’s start with this. Here, in no particular order, is a list of some things that I’ve said in the past, and which I still think are or should be of interest today. It’s meant to be background for numerous posts I write in the near future, and indeed a few hooks for such posts are included below.

1.  Data(base) management technology is progressing pretty much as I expected.

• Vendors generally recognize that maturing a data store is an important, many-years-long process.
• Multiple kinds of data model are viable …
• … but it’s usually helpful to be able to do some kind of JOIN.
• To deal with the variety of hardware/network/storage arrangements out there, layering/tiering is on the rise. (An amazing number of vendors each seem to think they invented the idea.)

2. Rightly or wrongly, enterprises are often quite sloppy about analytic accuracy.

• My two central examples have long been inaccurate metrics and false-positive alerts.
• In predictive analytics, it’s straightforward to quantify how much additional value you’re leaving on the table with your imperfect accuracy.
• Enterprise search and other text technologies are still often terrible.
• After years of “real-time” overhype, organizations have seemingly swung to under-valuing real-time analytics.

3. Outside traditional enterprises, the accuracy problem can be even worse, and the consequences of analytic inaccuracy can be severe. In some cases this is well understood; autonomous vehicle researchers, for example, seem properly attentive to the challenge of not-killing-pedestrians. But in others it’s a mess. For example, I don’t think the “fake news on social media” challenge will be resolved without new technical approaches that, to my knowledge, aren’t yet even being tried.

4. More generally, I’ve long argued that the technology industry would someday have to deal with a variety of public policy and social concerns. That day has come. In anticipation, I wrote at length about privacy/surveillance, and a little about some other areas, including net neutrality, patents, economic development, and public technology spending. Missing subjects include censorship (private and public alike), and perhaps also the efforts to tie data ownership into anti-trust policy.

5. Given all the tech-specific public policy work that’s needed, I’m pulling back from some my broader political efforts. However, I stand by my overview opinions of last February, and I delivered on some of its IOUs in a two-part series on persuasion.

6. The ongoing rise of “edge computing” and the “Internet of Things” fit into the general trend that in 2013 I summarized as appliances, clusters and clouds.

7. I continue to think that a huge fraction of analytics is properly characterized as monitoring. That ties into a number of areas of interest. For example:

• Platform technologies — including distributed data management — are often compete on the maturity of their built-in monitoring.
• My complaints about BI inaccuracy commonly relate to use cases in monitoring.
• Privacy/surveillance issues are commonly about monitoring. It’s common to worry that such monitoring is actually too accurate.
• But I also worry that privacy/surveillance monitoring isn’t accurate enough … and hence that it leads to people being discriminated against who absolutely don’t need to be.
• Edge computing involves a lot of devices that need to be monitored.
• Censorship obviously has a lot to do with monitoring.

8. And finally for now, my core precepts for strategic messaging haven’t changed.

Related link

• As you may have already guessed, the title of this post is based on a classic song.

1. Most of what people are saying about modern tribalism is correct. But partisanship is not as absolute as some fear. In particular:

• There are populist concerns on the right and left alike.
• Partisans of all sides can be concerned about privacy, surveillance and government overreach.

2. The threat from Trump and his Republican enablers is indeed as bad as people fear. He’s a major danger to do terrible, irreversible harm to the US and the rest of the world. To date the irreversible damage hasn’t been all that terrible, but if Trump and his enablers are given enough time, the oldest modern democracy will be no more.

All common interests notwithstanding, beating Trump’s supporters at the polls is of paramount importance.

3. I agree with those who claim that many of our problems stem from the shredding of trust. But few people seem to realize just how many different aspects of “trust” there are, nor how many degrees there can be of trustworthiness. It’s not just a binary choice between “honest servant of the people” and “lying, cheating crook”.

These observations have strong analogies in IT. What does it mean for a system to be “reliable” or to produce “accurate” results? There are many possible answers, each reasonable in different contexts.

4. I also agree with the view that much of what’s going on relates to lacks of empathy. But it’s not quite as simple as saying that liberals/Democrats/globalists have more empathy, while conservatives/Republicans/populists/nationalists have less. Populists and white nationalists likely have more empathy than I do for certain segments of the population, and anti-abortion zealots surely outshine me in empathy for fetal tissue.

Some say our troubles are due to a deliberate war on truth and democracy. Some say they’re just consequences of broad, long-running trends. I think both views are partially correct.

5. Much is made these days of people’s penchant for irrationality, which generally takes the forms:

• Irrational choices as to which factual claims to accept.
• Irrational conclusions in light of their chosen “facts”.

I think that a lot of this irrationality can be explained as people not taking the trouble to gain all the facts, to think things through, etc. Indeed, perfect rationality takes so much effort that it would be … well, that it would be a highly irrational choice. So if we want people to be more rational, perhaps we should make it easier for them to be so.

That challenge has many different facets. I hope to have something useful to say about it later on.

6. Outright changing somebody’s mind is very, very hard. But making them less sure of their opinion? That’s a lot easier. Making them more sure of it? That’s a reasonable goal as well.

This too will be spelled out in a future post.

7. Much of the prevailing irrationality can be modeled by describing which contradictions/doublethink people accept, and in which cases they think a contradiction actually proves that something is untrue. And people’s views are sometimes actually influenced by a pull to be more consistent. Real-life examples include:

• Some traditionally “law and order”/authority-following right-wingers who believe the current allegations about the “Deep State” are more open to doubting FBI claims in general.
• Similarly, the recent FISA legislation needed bipartisan support to pass, because some generally government-skeptical Republicans were in particular skeptical of the alleged national-security reasons for domestic snooping.
• States-rights supporters (who usually are conservatives) can extend that to disapproving of Federal marijuana laws and crackdowns.

• Vented, ranted and so on. That’s somewhat therapeutic, and also let me engage the other side and try to understand a little better how they think.
• Tried to understand what’s happening. I probably have had more available time to do that than most people. I also have a variety of relevant experiences to bring to bear.
• Neglected my work somewhat while doing all that. This neglect has now stopped. After all, the future is quite uncertain, so we should probably work hard in the present while business is still good.
• Written up some of what I’ve figured out. Of course. That’s what I do. But it’s only “some”, because … well, the entirety of politics is overwhelming.
• Tried to find specific, actionable ways to help. Stay tuned for more on that part.

As for those writings:

• I just posted a very high-level overview of modern political complexities. Please read it.
• I’m working on posts drilling down on various parts of that. Closest to readiness (Edit: Now written) are ones on “Modifying beliefs” (which will include some technology marketing advice) and “The war on truth and democracy” (which will argue that part — and only part — of what’s going on is properly described by the “war” metaphor).
• I recently posted that the tech industry is under broad political attack. That’s even more true than I realized. Two recent and indicative developments are:
  • Roger McNamee et al. have started an organization to combat the addictive evils they perceive the tech/internet industry as doing.
  • George Soros — whose organization was once my best-paying investment client — thundered at Davos that the tech/internet industry should and will be brought down by antitrust regulators.
• I also posted recently about the chaotic politics of privacy. If anything, the ongoing FBI/FISA firestorm suggests that I understated the matter.

Worldwide

The politics of privacy and surveillance are confused, in many countries around the world. This is hardly surprising. After all:

• Privacy involves complex technological issues. Few governments understand those well.
• Privacy also involves complex business issues. Few governments understand those well either.
• Citizen understanding of these issues is no better.

Technical cluelessness isn’t the only problem. Privacy issues are commonly framed in terms of civil liberties, national security, law enforcement and/or general national sovereignty. And these categories are inherently confusing, in that:

• Opinions about them often cross standard partisan lines.
• Different countries take very different approaches, especially in the “civil liberties” area.
• These categories are rife with questionably-founded fears, such as supposed threats from terrorism, child pornographers, or “foreign interference”.

Data sovereignty regulations — which are quite a big part of privacy law — get their own extra bit of confusion, because of the various purposes they can serve. Chief among these are: 

• Preventing foreign governments or businesses from impinging citizens’ privacy.
• Helping their own governments impinge on citizens’ privacy.
• Providing a pretext to favor local companies at the expense of foreign ones.

The United States

Specifically in the United States, I’d like to drill into two areas:

• An important bit of constitutional confusion.
• Just how bipartisan this all gets in our generally hyper-partisan times.

The constitutional confusion goes something like this:

• A new communication technology is invented, such as telephones or email.
• The courts rule that there is no Fourth Amendment expectation of privacy in using such optional services, because:
  • Given how the technology works, the information is temporarily under a third party’s control.
  • If you weren’t willing to give up your privacy, you wouldn’t have used the technology in the first place.
• Later the technology becomes so central to everyday life that courts start finding the previous reasoning to be inaccurate, and extend the Fourth’s protection of your “papers and effect” to the new communication medium.
• In the meantime, laws are passed regulating privacy for that particular medium.

For example:

• The Electronics Communications Privacy Act of 1986 (ECPA) — specifically in its section known as the Stored Communication Act (SCA) — gives authorities warrantless access to online information more than 180 days old.
• The Warshak decision of 2010 rendered the ECPA/SCA unconstitutional in the Sixth District.
• The Email Privacy Act was supposed to make Warshak the general law of the land.

Those links are all to Wikipedia. At the time of this writing, the ones on Warshak and the SCA go into considerable constitutional depth.

The Email Privacy Act is also the single best example of this post’s premises about the general chaos of privacy politics.

• It passed the House of Representatives unanimously in 2016 — 419-0 — which is an honor usually reserved for such noncontroversial subjects as renaming post offices.
• Even so, it was shot down in the Senate, under opposition from Senators of both parties,* never coming up for vote.
• It was passed by voice vote in the House again in 2017.
• It again didn’t come up for vote in the Senate.

Last week’s FISA reauthorization is another example; it wouldn’t have passed without senior-level Democratic support in the House and Senate alike.

*A chief opponent among the Democrats was Diane Feinstein, who despite representing California is commonly hostile to technological good sense. She voted for FISA reauthorization as well.

Like many folks, I’ve been distracted by all the other political calamities that have befallen since November, 2016. But the time to refocus on privacy/surveillance is drawing near.

Related links

• I wrote about similar subjects in May, 2016, and offered many links then.