The nonprofit sector is all a-twitter about the coming “third wave” of AI-fueled philanthropy, inspired by an article from Nan Ransohoff. The debates range from “this is the biggest thing ever to hit philanthropy,” to “it will just be business as normal” (giving away ~1%) or “it will be a bust.”

I was just reading a summary of all of the debates, thanks to Nick Martin of TechChange, and I was inspired to test out these options.

I am hereby announcing the foundation of the AI Billions Fund.

Other than the fact that I don’t have a billion dollars, I am reasonably well qualified for this.

• I started two for-profit AI companies in Silicon Valley back in the 1980s, both of which were acquired by public companies.
• I’ve run tech for good nonprofits ever since then, demonstrating how AI can benefit much more of humanity.
• My recent book, Technology for Good: How Nonprofit Leaders Are Using Software and Data to Solve Our Most Pressing Social Problems, is the first book to talk about how to start a nonprofit tech company. In it, I feature 60+ nonprofits using tech (and mostly AI) to meet social needs in spite of limited opportunities to get rich.

Based on my experience, and the hundreds of nonprofits I have had the honor to meet and often help, I know exactly how I would spend a few billion. And, now is your chance to bet on the AI Billions Fund!

The Investment Thesis

I’ve done and seen a lot of venture backed for-profit startups in Silicon Valley, many of which have made money. I’ve done and seen many exciting nonprofits using tech to deliver dramatic social impact.

What I haven’t seen is for-profits delivering venture returns by focusing on the problems of the poorest 90% of humanity.

For fairly obvious reasons, profit-maximizing is tough to pull off when your target customers have next to no money. OpenAI’s pivot from being a nonprofit to a for-profit is just one of a long list of stories about why a primary focus on social impact is much easier to deliver via a nonprofit.

If the benefits of AI are really going to flow to most of humankind while preserving their agency and humanity, we’re going to need a lot more investment in tech for good nonprofits. Nonprofits with tech teams, and their eyes on the social impact prize. Not because they are hoping to get obscenely wealthy, but because they choose to work on more important problems while (hopefully) making a good living.

I’ve met these leaders.

• They’ve found product-market fit.
• They have gained the trust of the communities they serve.
• They have gained the knowledge of how the real world works, and how to change it.

What they haven’t gotten?

Enough money to truly scale their impact. Money that allows them to build their teams and technology. And crucially, to build up the critical data assets to effectively apply AI to these pressing social problems, which barely exist today.

The foundation world doesn’t know what to do with these tech nonprofits. There are few foundations that are actually good at assessing technology social enterprises.

So, my investment thesis is that the social return on investments in nonprofit tech enterprises will be some of the top returns imaginable in philanthropy. When you create an AI/software product that can do twice as much good (or five or ten times) as status quo approaches with a given amount of funding, you get the digital equivalent of inventing a drug that cures a disease.

How Would I Spend a Billion

Here is where I would invest a sizable amount of philanthropic capital:

• Invest seven figure grants in tech for good nonprofits who have already found product/user fit at scale. More than a hundred come to mind, and I assure you they can absorb the money.
• Build critically needed core infrastructure to support AI (and invest in expanding nonprofit data capacity required for AI). It is obvious to me that there is a desperate need for a pipeline to take the latest open source LLM models and adapt them to the needs of the social sector. Building versions to run these on commodity cloud infrastructure and locally on commodity PCs both with and without GPUs.
• Invest in large scale datasets relevant to addressing social problems, under responsible data governance and non-commercial control, such as the free Better Deal for Data standard I helped launch this year.
• Invest in talent close to the social problems being solved. There is no reason Africa, Latin America, or Asia need to import U.S. talent at U.S. price points.

There are a handful of great (and new in the last ten years) funders already working on these priorities. There are far from enough of them.

The Patrick J. McGovern Foundation. The Agency Fund. Co-Develop (a $130M fund backed by Nilekani and Gates) is already investing in open source projects in digital public infrastructure, a narrow set of essential baseline capabilities (identity, payments, data exchange).

The AI Billions Fund would likely co-invest with many of these, but also reach great organizations that fall outside the specific objectives of these funders.

Call to Action

If you are a tech leader on the brink of billions (or already have them), get in touch with me. My free one-session karma consults are renowned in the field. You should have a sizable commitment to the AI Billions Fund in your initial philanthropic impact portfolio, run by someone who has actually been a successful serial tech entrepreneur.

Whether or not you decide to invest in the Fund, I’d be delighted to share my list of outstanding tech for good leaders who need your support!

By Jim Fruchterman, an author, leading social entrepreneur, MacArthur Fellow, and founder of Benetech and Tech Matters, nonprofit technology companies based in Silicon Valley.

The post Announcing the AI Billions Fund for Technology Nonprofits appeared first on ICTworks.

The privacy consent model assumes a person who reads the notice, understands it, and chooses. For the hundreds of millions of people coming online for the first time, that person does not exist.

For two decades, our fix for weak privacy protection in emerging markets has been the same: better notice. Make the consent screen shorter, clearer, translated into the local language, and the user will make an informed choice.

India just wrote that theory into binding law.

The Digital Personal Data Protection Rules, notified in November 2025 with an 18-month phased compliance window, require every company processing personal data to issue a separate consent notice in clear and plain language, available in English or any of the 22 languages in the Constitution’s Eighth Schedule. The Principles for Digital Development say much the same thing in Principle 8. It is a reasonable theory.

A new peer-reviewed study of 50 first-time smartphone users in urban India shows that, for this population, it is mostly beside the point.

The study, “I Just Press Allow”: Understanding Privacy Practices of New Internet Users in Urban India, interviewed 50 early-stage smartphone users across urban and peri-urban India. Their finding, in their own words, is a mismatch between formal privacy controls and the realities of everyday practice.

This matters at scale. India crossed 886 million active internet users in 2024, with rural users now the majority and 98 percent consuming content in Indic languages rather than English. India is not unique.

The GSMA’s Mobile Gender Gap Report 2025 finds the same barriers, affordability, low digital skills, and safety concerns, at their widest across South Asia and sub-Saharan Africa, where roughly 60 percent of the world’s unconnected women live.

The people the consent model fails are the ones powering smartphone growth.

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Person Who Clicks Is Often Not the User

The first thing the study breaks is the assumption that there is one user, sitting at the device, making a decision. Again and again, the person who installed the app, granted the permissions, and dismissed the prompts was someone else: a son, a nephew, a neighbor, a shopkeeper.

One woman handed her phone to her son whenever a new prompt appeared. He clicked something, handed it back, and she trusted that he had handled it. Another judged an app safe because her nephew said everyone uses it. A third let the shopkeeper who sold her a loan app grant camera and SMS access because he said it was normal.

The authors call this interdependent privacy, and no amount of training fixes it. It is a structural feature of how phones enter a household.

Skills concentrate in the digitally literate relative, usually a younger man, and everyone else delegates. Translating the consent notice into Telugu does nothing for the 56-year-old whose nephew taps Allow before she ever sees the screen.

The consent UI is built for a decision-maker who, in these homes, is not the data subject. This is the same gap Nithya Sambasivan’s team documented across India, Pakistan, and Bangladesh, where women’s phones are routinely shared and surveilled by family.

Seven years later, our consent design still assumes the lone individual.

When Refusal Is Not an Option

The study’s title comes from a construction worker who tried to deny permissions to a ride app. It refused to open. She tried again, gave up, and allowed everything.

Since then, she presses Allow on every app, because otherwise they do not work. She told the researchers she feels like she is not really choosing.

She is correct.

The authors call this compelled consent, and it is the part of the paper that should end the “better notice” conversation.

When denying a permission breaks the app, and the app is how you book transport or send money, refusal is not a choice the interface offers. A clearer notice changes nothing about that. The user already understood the trade. She had no alternative.

This is a sharper version of the digital resignation that privacy scholars have described in Western markets, except here it is compounded by dependency and the fact that the essential service and the data grab are bundled into the same button.

Permission-gating of core functionality, where the app holds itself hostage until you consent, is a coercive design.

Independent research suggests apps routinely ask for more than they need: NordVPN’s analysis of top apps found roughly a fifth of requested permissions were not required for the app to function, with nearly half of apps requesting access tied to activity outside the app itself.

Treat that as directional rather than precise, since it is a vendor study, not peer-reviewed. The point holds: the consent screen is the thinnest possible cover for a demand the user cannot refuse.

DPDP Act Framework

India’s DPDP framework is a genuine advance, built on the Indian constitutional right to privacy the Supreme Court recognized in 2017. Plain-language notices, multilingual access, consent withdrawal links, and a Data Protection Board are all worth having.

But every one of those provisions optimizes for comprehension. The study shows comprehension was not the thing standing between these users and protection.

The binding constraints were two: someone else made the decision, and refusal was not possible. Neither is addressed by a clearer notice in a 22nd language.

If you fix readability and leave delegation and compulsion untouched, you will have built a more legible version of the same trap, and you will be able to say consent was obtained. That is the risk: a compliance regime that measures notice quality and declares victory while the woman handing her phone to her nephew is exactly as exposed as before.

The Threat Model Is Social

We treat privacy as a question of data flows: who collects what, stored how long, shared with whom. The users in this study do not experience it that way.

The harms they named were a ride driver who saved a passenger’s number and called at odd hours, a woman whose photo was lifted from Facebook onto a fake profile, a relative tricked into entering her Aadhaar number into a fake government form.

For women especially, a breach means harassment, blackmail, and reputational damage with physical consequences.

This is why notice-and-consent protects against the wrong threat. The same lesson surfaced when researchers found that Facebook’s privacy concepts did not translate into Khmer for Cambodian users, and it sits at the center of the long-running question of data justice for the next billion users.

A consent model imported from a context where the feared adversary is a corporate data broker will keep missing the adversary these users fear, who is often a stranger, a relative, or a repair technician.

Users Already Build Workarounds.

The most useful finding is that these users are not passive. Denied real controls, they built their own.

• They swap personal photos for flowers and cartoons.
• They post poems and festival greetings instead of selfies.
• They run second accounts to wall off relatives, lock apps behind patterns, hide folders, and clear browser history.

One 19-year-old learned app-locking from a YouTube video. The authors call this pragmatic privacy and tactical agency, and it is real expertise, invisible to the designers who never asked.

That is the design brief, and it is the opposite of where most of our informed consent thinking points. Stop trying to turn these users into the rational individual the consent screen imagines.

Build for the user who is already there: who shares a device, delegates setup, and fears social exposure more than data brokers.

What Informed Consent Demands

MeitY and the Data Protection Board of India should not stop at notice quality.

They should require shared-device and delegation-aware consent, including guest modes, role-based profiles, and re-consent prompts when a device changes hands, and they should treat permission-gating of core functionality as a consent-defeating dark pattern, not a developer’s prerogative.

India’s consent notices can now be served in any of 22 official languages. The woman whose nephew taps Allow before handing the phone back will still never read one.

The post The Next Billion Users Will Not Read Your Consent Notices appeared first on ICTworks.

Are we building AI systems backwards, evaluating them wrongly, and then wondering why our multi-million dollar algorithms fail to deliver the outcomes we promised donors?

After analyzing the latest research from the Center for Global Development alongside recent humanitarian AI deployments, I’m convinced that our sector’s approach to AI evaluation is fundamentally flawed. And that is killing good projects before they even have a chance to succeed.

The problem isn’t that humanitarian AI lacks potential.

• Danish Refugee Council’s displacement forecasting models can predict population movements ,
• UNHCR’s Project Jetson successfully anticipates forced displacement in Somalia,
• WFP’s AI Sandbox provides a robust testing platform for internal projects.

The problem is that we keep jumping straight to impact evaluations before ensuring our AI systems function properly—a costly mistake that’s become endemic across the sector.

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We’re Measuring the Wrong Thing

Asking “Does this AI chatbot reduce infant mortality?” before confirming “Does this chatbot provide medically accurate responses?” is professional malpractice.

Yet this is exactly what humanitarian organizations do when they rush into randomized controlled trials for AI systems that haven’t been properly validated at the technical level. Spoiler alert: this backwards approach is why so many AI pilots never make it to scale.

We have over 53 published ethical AI guidelines in humanitarian contexts, yet a 2024 Wilton Park report found that many AI applications are still being deployed without sufficient evidence of their effectiveness, feasibility, or ethical implications.

This is actively harmful. When poorly evaluated AI systems fail in the field, they don’t just waste resources. They erode community trust, endanger vulnerable populations, and set back the entire humanitarian technology agenda.

The Four-Level Solution That Works

The Center for Global Development’s new evaluation framework offers a systematic alternative to our current chaos. Instead of jumping to impact measurement, they propose four sequential evaluation levels that build credible evidence step by step.

Here’s how humanitarian organizations should implement this framework:

Level 1: Model Evaluation – Does Your AI Behave Correctly?

Before deploying any AI system, you must verify it performs its intended function accurately and consistently. For humanitarian AI, this means rigorous testing against expert-validated responses.

Take medical AI chatbots serving refugee populations.

Level 1 evaluation requires creating reference datasets where medical experts provide ideal responses to common health queries. The AI’s outputs are then scored against these references for accuracy, empathy, and appropriateness. If your AI chatbot recommends dangerous treatments or provides culturally insensitive advice, you’ll discover this during controlled testing—not after deployment in a refugee camp.

The key insight from CGD’s research is that unlike human-operated services, AI allows rapid modification and testing cycles. Organizations should leverage this advantage by conducting multiple evaluation rounds with different prompts and configurations before settling on a final model.

Level 2: Product Evaluation – Are People Using It?

Technical accuracy means nothing if your target users abandon the system after first contact. Level 2 evaluation focuses on engagement metrics and user experience optimization.

Humanitarian organizations need to track user interaction patterns systematically.

• How long do displaced persons spend interacting with your AI information system?
• Do they return for follow-up queries?
• Are usage patterns consistent across different demographic groups?

Tools like IDinsight’s Experiments Engine and Agency Fund’s Evidential platform enable rapid A/B testing of different interface designs, conversation flows, and content strategies. This level of evaluation often reveals critical barriers—like language preferences or cultural communication norms—that technical testing misses entirely.

Level 3: User Evaluation – Is It Changing Behavior Appropriately?

Even high engagement doesn’t guarantee meaningful outcomes. Level 3 evaluation assesses whether AI interaction produces the cognitive, affective, and behavioral changes needed for downstream impact.

For AI-powered health education systems, this means measuring whether users gain accurate knowledge, feel confident about health decisions, and take appropriate preventive actions. These intermediate outcomes serve as early indicators of eventual health impact while remaining faster and cheaper to measure than clinical endpoints.

This evaluation stage typically combines usage analytics with user surveys, qualitative interviews, and behavioral tracking. The goal is confirming that your AI system creates the psychological and behavioral preconditions necessary for ultimate impact.

Level 4: Independent Impact Evaluation – Does It Improve Lives?

Only after confirming technical reliability, user engagement, and behavioral change should organizations invest in randomized impact evaluations. At this stage, you’re measuring whether AI-enhanced interventions produce better development outcomes than traditional approaches.

The Brazilian state of Espírito Santo exemplifies this approach.

They piloted the Letrus AI writing platform with rigorous evaluation by J-PAL researchers, finding that students wrote more essays, received higher-quality feedback, and scored better on national writing tests. Based on this evidence, they expanded statewide—a decision justified by systematic evaluation across all four levels.

Why Sequential Evaluation Saves Money and Lives

This framework prevents the expensive mistakes I’ve seen across the humanitarian sector. Consider what happens when organizations skip early evaluation levels:

• Without Level 1 evaluation, you deploy AI systems with unknown accuracy rates. The International Review of the Red Cross warns that biased AI systems can perpetuate discrimination against already vulnerable populations—discrimination that goes undetected without proper technical evaluation.
• Without Level 2 evaluation, you build systems that technically work but nobody uses. This is particularly problematic in humanitarian contexts where digital literacy varies widely and user interface assumptions often prove incorrect.
• Without Level 3 evaluation, you deploy systems that people use but that don’t change behavior in meaningful ways. Users might enjoy interacting with your AI health advisor while continuing harmful practices.

Following this sequential approach transforms abstract discussions about AI reliability into concrete, evidence-based decisions that can inform both program design and policy development.

The post How to Evaluate Artificial Intelligence Systems for LMIC Usage appeared first on ICTworks.

Early-career African drug discovery researchers writing in the Journal of Medicinal Chemistry put Africa’s share of global scientific output at 2 to 3 percent, against a continent holding more than 85 percent of the world’s neglected tropical disease cases.

Scientific output can increase with a hands-on mentoring program designed to support scientists who want to apply open-source AI tools to their drug discovery projects for infectious diseases.

AI Incubator for African Principal Investigators

AI Incubator is a six-month program with biweekly mentoring, sponsored travel, and a $10,000 fellowship for senior researchers, or teams of up to four scientists, at an academic or nonprofit institution in the WHO African Region.

They should be using AI in an existing drug discovery project on small molecules, including isolated natural products, for infectious diseases such as malaria, tuberculosis, Gram-negative infections, or neglected tropical diseases. PhD students can lead an application with written PI support.

The fellowship covers laptops, internet costs, conference registration, article processing charges, reagents, molecule purchases, and stipends. Teams keep ownership of their data and results under an agreement that cites the African Union Data Policy Framework.

Apply Now: Deadline is October 19, 2026.

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I have a philosophical question for you to ponder:

If a research paper is published, and its hypothesis is proven, but no one acts on it, did the paper even exist?

I ask this because I’ve seen too many academic research reports that are written by and for scientists, that prove a theory but are lost to history because they are written in obtuse jargon and published to obscure journals.

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Investing in a PDF Graveyard

We are spending millions of dollars to produce digital tombstones that nobody visits, while the constituents we serve couldn’t care less about yet another peer-reviewed journal article or its citation count.

This is the PDF Graveyard.

Researchers and their organizations only seem to count the number of reports they publish, not the impact of their research.This isn’t just an academic gripe. It is a systemic failure in the international development sector.

When did a poor farmer, a pregnant mother, or caring parents ever ask you if your intervention was published in a peer-reviewed journal?

The Rising Tide of Unread Words

Let’s look at the United Nations numbers, because the scale of this waste is staggering.

In 2024, the United Nations Secretariat published more than 1,100 reports. This represents an increase of nearly 20 percent since 1990. We are producing more content than ever before, yet we aren’t necessarily communicating more effectively.

The problem isn’t just the quantity of reports; it’s their bloat. Reports are growing longer, with average UN report word counts rising by 40 percent since 2005. If you look at reports going to the General Assembly, Security Council, and ECOSOC, they now average around 11,300 words each. That is about one-third longer than the UN Secretariat’s own recommended limit.

We are essentially asking busy policymakers and practitioners to find the signal in an ever-growing mountain of noise. When everything is comprehensive, nothing is a priority. This “more is better” approach to reporting is actively undermining our ability to drive change.

$360 Million for Unread Reports

Some might argue that these reports are core to the UN’s mission of norm-setting and dialogue. I don’t disagree that information exchange is critical. However, the efficiency of this exchange is abysmal.

In 2024, the UN Secretariat spent over $360 million on direct costs associated with meetings and reports. This includes things like editing and translation, and it represents more than 10 percent of the Regular Budget. Keep in mind, this doesn’t even include the indirect costs, like thousands of hours spent on research and drafting, which would raise that figure considerably.

Is this a good use of $360 million? The data suggests otherwise.

Despite the vast output, most reports are not widely read. Last year, nearly 65 percent of these reports were downloaded fewer than 2,000 times. Compare that to the top 5 percent of reports, which were accessed at least 5,500 times.

The truth is more complex than just download stats, as some argue that reports are emailed or shared through other channels. But even then, the World Bank found that 87 percent of policy reports were never cited.

If a report isn’t downloaded and it isn’t cited, it’s hard to claim it’s having any impact at all. We are essentially paying for the privilege of talking to ourselves.

Why Publish to PDF Graveyards?

Why does this continue? It comes down to misaligned incentives.

Too many organizations consider publication to be the end goal. They view the work of turning research into impact as someone else’s job. In many academic and policy circles, the “publish or perish” mentality reigns supreme.

ICT4D researchers are failing the poor by not engaging closely enough with the users of their research. The reality is that change takes time and effort to spread key messages and build champions at all levels of an ecosystem.

A PDF alone, no matter how brilliant, cannot do that.

From “Publish or Perish” to “Impact or Die”

The defunding of international development is an appropriate moment to review this situation. We need to stop equating publication with progress. For our constituents, published reports that are not acted on are a waste of everyone’s time and money.

We need to move from “publish or perish” to “impact or die”.

Here are three things we must do differently:

1. Prioritize Impact Over Volume: We need to streamline requests for reports and prioritize quality and actionability over word count. We should experiment with different formats, rather than default to the 10,000-word monolith.
2. Align Funding with Outcomes: Donors need to move away from demanding reporting bloat. We need more flexible, pooled funding that allows organizations to focus on achieving measurable outcomes for people and the planet.
3. Invest in the “Last Mile” of Research: If we spend $1 million on a study, we should be spending an equal amount on the advocacy, communication, and implementation support needed to make that study matter. Impact should be considered equal to scientific discovery, not a secondary “nice-to-have”.

Let’s be honest: no one is going to miss the 1,101st report from the UN. But they will notice if we start spending $360 million on things that change lives.

It’s time to stop building graveyards and start building impact.

The post Are Your Publications Creating PDF Graveyards and Failing the Poor? appeared first on ICTworks.

Here’s what I find maddening about the current state of AI governance in humanitarian organizations: we’ve got 80% of aid workers using AI tools daily while fewer than 25% of their organizations have any formal AI policies.

That’s organizational negligence disguised as prudence.

Too many humanitarian organizations are paralyzed by the complexity of AI governance, creating elaborate ethics committees and month-long approval processes that accomplish nothing except slowing down deployment while staff continue using ChatGPT and Claude without any guidance whatsoever.

Meanwhile, Charles Sturt University quietly released the SECURE framework that turns AI risk assessment into a simple six-question checklist.

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Governance Theater vs. Practical Solutions

Let’s be honest. Recent research from the Humanitarian Leadership Academy reveals that despite widespread individual adoption, only 8% of organizations report widespread AI integration and 64% provide little to no AI training for staff.

This creates exactly the wrong incentives.

Staff are using AI tools anyway. They just can’t be transparent about it or get institutional support when things go wrong. Organizations pretend they’re being “responsible” by having lengthy AI ethics discussions while their people are already deep in the AI adoption curve, making decisions with commercial tools that may not align with humanitarian principles.

I’ve lost count of how many humanitarian technology conferences I’ve attended where panels spend 45 minutes discussing the philosophical implications of AI bias while field staff in the audience are quietly wondering whether they can use Claude to translate beneficiary interviews without violating data protection policies.

Why AI Governance Frameworks Fail

The fundamental problem with most AI governance approaches is they’re designed by committees for committees. Look at the 53+ different AI ethics guidelines floating around the development sector—each one longer and more abstract than the last.

These frameworks typically fail because they:

1. Require expertise organizations don’t have: Most guidelines assume deep technical knowledge about AI systems that humanitarian organizations simply don’t possess.
2. Create approval bottlenecks: Complex review processes mean simple use cases get stuck for weeks while genuinely risky applications slip through with minimal scrutiny.
3. Focus on perfect solutions: Academic approaches that demand comprehensive impact assessments for every AI use case guarantee that nothing gets deployed.
4. Ignore operational reality: Beautiful principles that fall apart the moment you try to apply them in a refugee camp with intermittent internet and three overworked program officers.

SECURE Framework: Just Six Questions

SECURE framework takes a radically different approach. Instead of abstract principles, it provides six concrete yes/no questions that any humanitarian worker can answer:

1. Security credentials: Are you entering login details, passwords, or API keys?
2. Ethical use: Are you working with vulnerable populations or rare cultural knowledge?
3. Confidential information: Are you sharing data vital to organizational competitive position?
4. Use of personal information: Are you processing personally identifiable information?
5. Rights protection: Are you using copyrighted materials without permission?
6. Evaluation of outputs: Are you using AI output without human review for critical decisions?

If you answer “no” to all six questions, you’re cleared to proceed. If you hit “yes” on any category, you pause, try to mitigate the risk, and escalate to IT if necessary. That’s it.

No committees, no month-long reviews, no philosophical debates about the nature of algorithmic bias.

How SECURE Accelerates Deployment

This is where the framework gets clever. By clearly defining what requires approval, it dramatically speeds up everything that doesn’t.

Currently, most humanitarian organizations operate under blanket uncertainty. Staff assume they need permission for everything AI-related, so simple use cases get stuck in approval processes designed for complex ones.

The SECURE framework flips this dynamic.

Staff can confidently use AI for report writing, translation, data analysis of anonymized datasets, and content generation without waiting for organizational approval. This covers probably 80% of current humanitarian AI use cases.

Meanwhile, truly risky applications, like processing beneficiary personal data or making resource allocation decisions, get flagged for proper review. The framework channels organizational oversight energy where it’s needed instead of spreading it thin across every possible use case.

Why This Matters for ICT4D Practitioners

The humanitarian sector’s AI paradox of high individual adoption, yet low organizational support, is a classic implementation failure that we’ve seen repeatedly in ICT4D solutions. It’s the same pattern we saw with mobile money, social media adoption, and cloud computing integration.

Organizations that figure out practical governance frameworks first will have enormous advantages. They’ll be able to harness their staff’s existing AI skills, deploy solutions faster, and iterate based on real use cases rather than theoretical scenarios.

I predict that humanitarian organizations using frameworks like SECURE will be deploying AI solutions 3-6 months faster than competitors stuck in governance theater. In crisis response, that speed advantage translates directly to lives saved and resources optimized.

The post Use This Simple AI Framework to Stop Drowning in AI Governance appeared first on ICTworks.

AI-native African companies took less than 2% of the continent’s startup funding in the first half of 2026, Grégoire de Padirac, CEO of Digital Africa, told BusinessDay. Over the same period, the number of African startups raising at least $100,000 fell to 190, the lowest since 2021.

$200,000 Opportunity

Askya AI Growth Platform is a six-week program for 10 AI startups based on the African continent. Selected founders get weekly one-on-one coaching, plus cloud and GPU capacity through partner infrastructure providers. Askya may invest up to $200,000 in the cohort’s most promising startup.

Moonshot by TechCabal hosts the cohort reveal in Lagos on October 28 and 29, before an audience Askya puts at 6,000-plus. Tosin Eniolorunda, founder and Group CEO of Moniepoint, is honorary chair of the first cohort.

Askya selects 10 startups meeting all five criteria:

• AI-native product. AI must be the core of the value proposition. A feature added to an existing product does not qualify.
• Commercial traction. A live product with at least one paying customer or an active pilot.
• Africa-focused. Based anywhere on the continent, building for African markets.
• MVP to Series A. A working product in market, through to Series A.
• Availability. Founders can commit 3 to 6 hours a week, in English, October 26 to December 4.

Nonprofits and research institutions do not qualify. Askya wants commercial startups with revenue.

Apply Now: Deadline is September 30, 2026

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The UK’s Digital Development Strategy 2024 to 2030 is still live on GOV.UK. Four years from its deadline, I cannot find a published pound behind it.

The comfortable reading in our sector is that the strategy survived. Nobody withdrew it. We covered its launch as a real step forward, and in my reading it remains the most specific digital development strategy any bilateral donor has put in writing.

Partner governments built plans around it: the strategy documents UK technical assistance behind Nigeria’s National Broadband Plan and support to Kenya’s telecom regulator on its Universal Service Fund. A strategy that has not been rescinded is, by the usual logic, a strategy still in force.

That logic assumes the money follows the paper. In this case the paper is four years old and the money has been reallocated twice since.

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The Strategy Is 3 Governments Old

The foreword is signed by Andrew Mitchell, Minister for Development and Africa in a Conservative government. GOV.UK carries its own banner on the page: published under the 2022 to 2024 Sunak Conservative government. The page’s political status is classified as historic, which is a routine label, not a withdrawal.

Since Mitchell signed it, four people have held the development brief. Mitchell, then Anneliese Dodds, who resigned in February 2025 over the cut from 0.5% to 0.3% of GNI, then Jenny Chapman, and now Kirsty McNeill, appointed in July 2026 by Andy Burnham’s government and, unlike her predecessor, not attending Cabinet.

The strategy’s parent document has also gone. The DDS says explicitly that it takes forward the digital approach set out in the 2023 white paper on international development.

That white paper has been overtaken by the modern development approach FCDO published in July 2026, which names three spending priorities: humanitarian, health, and climate and nature, underpinned by economic development.

Digital is not one of them. It does not appear as a priority anywhere in that paper, which was written in the same year FCDO’s bilateral aid to Africa was cut by 56% against 2024/25 levels.

So we have a strategy whose author’s government is gone, whose minister is gone, and whose framing document has been replaced. It is still the operative statement of UK policy on digital development because FCDO has not issued another one.

Four Targets Are Still Expected

The DDS commits the UK to four specific, countable things by 2030:

1. Support at least 20 partner countries to reduce their digital divides by an average of 50%, halving their connectivity gap.
2. Support at least 20 partner countries to transform national digital service delivery through improved digital public infrastructure.
3. Create or scale at least 8 responsible AI research labs at African universities, and help at least 10 partner countries build AI regulatory frameworks.
4. Support at least 50 million women and girls to participate safely and meaningfully in the digital world.

These are good targets. They are specific, they name a number, and they are the kind of commitment that can be marked wrong. That is exactly what makes their current status worth asking about.

March 2026 Funding Allocations

FCDO’s ODA programme allocations for 2026/27 to 2028/29 contain five tables. Digital development appears in exactly one line.

The Global Tech thematic directorate receives £24 million in 2026/27, £22 million in 2027/28 and £24 million in 2028/29. That is £70 million across three years, against total FCDO programme ODA of £6,815 million in the first year alone.

Set that against a single existing commitment. FCDO’s flagship AI for Development programme, AI4D, is a £53 million FCDO contribution within a £63.6 million partnership with Canada’s IDRC, running to 31 March 2029. Its most recent published annual review scored it A+.

Is FCDO Now Going All-in on AI?

FCDO’s Global Tech directorate is allocated £70 million across three years. Its flagship AI programme alone is a £53 million commitment running through the same period.

Two caveats, both of which FCDO supplies and both of which cut in awkward directions.

First, the statement’s footnotes say thematic directorate allocations do not capture all ODA spent against a theme, because country and regional programmes deliver against themes too. That is true, and it is also the problem: country and regional programmes are precisely where the deepest cuts land.

Second, I cannot confirm that AI4D sits inside the Global Tech line rather than under Research and Analysis or the Global Research and Technology Development portfolio. FCDO has not published the mapping, and it has not published a consolidated digital figure since the last time we tried to assemble one.

If AI4D sits in Global Tech, one programme absorbs roughly half of it. If it sits elsewhere, FCDO has still not shown us where digital money lives. Either way, no digital-specific allocation exists in the published tables. The connectivity target, the DPI target and the women and girls target have no visible line at all.

Where Are the Delivery Vehicles?

The DDS names its instruments.

• It says the UK will deliver a new phase of the Digital Access Programme to spread DAP evidence and innovations to further partner countries.
• It says the UK will continue with GSMA through a new phase of the Mobile for Development programme.
• It says the UK will develop a new DPI knowledge-management project, and a new Digital Sustainability Programme.

DAP’s DevTracker record still lists the programme as in implementation with a planned end date of 31 March 2030. The strategy reports DAP had reached 10.2 million people across 3,282 communities, facilitated 102 policy or regulatory reforms, and helped five partner countries cut their digital gap by over 26%. That is a working programme with published results, which is rarer in our sector than it should be.

What I cannot find is any announcement of the new DAP phase, the new M4D phase, the DPI project or the sustainability programme. Searches of GOV.UK, FCDO press releases, DevTracker and the ITU partnership page in August 2026 turned up no launch, no procurement notice, and no budget line for any of the four.

That is a negative finding, not proof. Procurement can be quiet. Programmes get designed for eighteen months before anyone publishes. I am stating what the search found and did not find, and leaving the inference where it belongs.

Did FDCO Abandon Digital Development?

I cannot document that FCDO has abandoned the 2030 digital targets. No minister has said so. No revision has been published. The strategy sits on GOV.UK looking exactly as it did in March 2024.

What I can document is this. The strategy is live. Its four targets are unchanged. The published allocations contain one digital line worth £70 million over three years, most or all of which may already be committed to a single AI programme. Its named delivery vehicles have no announced successor phases. And the policy paper that now frames UK development spending does not mention digital as a priority.

A withdrawn strategy would be more honest than this.

When a donor withdraws a commitment, partner governments replan. When a donor leaves a commitment standing with no money behind it, ministries in Kenya, Nigeria, Ghana and Rwanda write national digital plans that cite UK support, budget against it, and discover the gap at implementation. I have watched that specific failure mode play out enough times to recognise the setup.

What FCDO Should Publish

Three things, and none of them requires new money.

1. State whether the four DDS 2030 targets stand. If they stand, publish the allocation. If they do not, say so and let partner ministries replan.
2. Publish where digital development sits in the March 2026 allocations. Which directorates, which country programmes, what total. The footnote saying thematic lines do not capture everything is only acceptable if FCDO shows what the everything is.
3. Publish the status of the four named DDS delivery vehicles: the next DAP phase, the next GSMA Mobile for Development phase, the DPI project and the Digital Sustainability Programme. Launched, delayed, cancelled. Three words each would do.

Four targets, four years, one line in the budget. Which of the four survives?

The post FCDO Promises to Halve the Digital Divide. Where Is the Money? appeared first on ICTworks.

The conventional wisdom about small and medium enterprises in developing countries sounds reasonable enough: build more fiber optic cables, expand mobile coverage, launch digital literacy campaigns, and eventually SMEs will adopt technology.

I’ve watched this supply-side playbook unfold across sub-Saharan Africa for years, with mixed results at best. But new research from Ethiopia’s Amhara region reveals fundamental flaws that should force us to completely rethink how we support SME digitization.

The truth is more complex than our infrastructure-first assumptions suggest. SMEs in low and middle-income countries aren’t resistant to technology. They’re constrained by ecosystem failures that no amount of additional fiber optic cable can solve.

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Evidence from Ethiopia, Bangladesh, and India demonstrates that when properly designed demand-side interventions address these ecosystem failures, SME technology adoption accelerates dramatically.

High SME Adoption, Universal SME Barriers

A comprehensive study of 44 registered SMEs in Bahir Dar and Gondar cities uncovered something remarkable:

• 93% of surveyed enterprises use multiple ICT tools including computers, internet, mobile phones, and cash registers.
• 82% leverage these tools across diverse business functions including financial reporting, transaction processing, and customer engagement.
• 87% agreed that ICT infrastructure improves SME performance,
• 100% acknowledged that deep ICT understanding drives sustainable economic development.

These aren’t technology-resistant traditionalists clinging to pen-and-paper bookkeeping. They’re digitally sophisticated businesses that recognize technology’s transformative potential.

Yet here’s the paradox. Every single respondent (100%) identified the same six systemic barriers constraining their digital transformation:

1. High telecommunications costs
2. Poor communication infrastructure
3. Technological illiteracy
4. Lack of qualified staff
5. Use of outdated systems
6. Absence of state support

This pattern isn’t unique to Ethiopia. It reflects ecosystem failures that demand-side policy interventions can address more effectively than continued supply-side expansion.

Bangladesh Demand-Side FinTech Revolution

Bangladesh’s transformation from cash-dependent to digitally-enabled economy illustrates how targeted demand-side interventions succeed where infrastructure expansion alone fails.

The government’s decision to distribute cash assistance to five million vulnerable poor families hit by the coronavirus pandemic through four major mobile financial services operators demonstrated that when ecosystem conditions align properly, SMEs rapidly adopt digital solutions.

The key insight is that policymakers addressed demand-side constraints systematically. Bangladesh Bank’s policy supports for inclusive and sustainable financing included massive up-gradation of the payment system and the financial sector IT infrastructure enabling the advent and rapid growth of cost-efficient off-branch online/mobile phone/smart card-based financial service delivery.

This comprehensive approach yielded remarkable results. Research on SME entrepreneurs found that performance expectancy, facilitating conditions, and hedonic motivation directly impact entrepreneurs’ satisfaction, which, in turn, influences their intention to continue using digital financial technologies.

The difference was ecosystem design that addressed user needs, regulatory frameworks, and institutional support simultaneously.

Government-Led Demand-Side Innovation

India’s Unified Payments Interface represents perhaps the most compelling evidence for demand-side policy intervention effectiveness. From processing minimal transactions in 2016 to more than 13 billion transactions per month as of March 2024, UPI now accounts for 82% of the volume of total digital payments in India.

The success wasn’t driven by superior infrastructure compared to other emerging markets. India’s mobile internet penetration and smartphone access lagged many countries when UPI launched. Instead, strategic government intervention addressed ecosystem barriers that constrained SME adoption.

Government policy eliminated cost barriers by waiving merchant discount rate charges on UPI transactions for merchants with annual turnover of up to ₹500 million ($6 million), while digital payment providers introduced a variety of zero-fee payment options.

This targeted intervention addressed the financing constraints that Ethiopian SMEs identified as universal barriers.

The results demonstrate demand-side intervention effectiveness at scale. Economic analysis shows a 1 percent increase in UPI transaction volume strongly correlates with a 0.03 percent increase in GDP growth, highlighting UPI’s role in economic expansion.

For SMEs specifically, research found that a 10% increase in UPI transactions resulted in a 7% increase in credit availability while loans to new credit borrowers have increased by 4%, while loans to subprime borrowers have increased by 8%.

M-Pesa: Early Demand-Side Pioneer

While not focused on SMEs specifically, Kenya’s M-Pesa provides additional evidence for demand-side intervention effectiveness.

The mobile money platform succeeded not because Kenya had superior telecommunications infrastructure, but because regulatory decisions created enabling conditions for financial innovation while traditional banking served primarily urban, affluent customers.

The ecosystem approach of combining regulatory sandbox policies, agent network development, and simplified know-your-customer processes, enabled rapid adoption by previously excluded populations, including small businesses. This contrasts sharply with purely supply-side infrastructure expansion that assumes usage will follow access.

The Demand-Side Policy Window

The Ethiopian data reveals a rare policy opportunity that challenges prevailing ICT4D orthodoxy.

Traditional supply-side interventions like expanding broadband access, reducing device costs, building digital infrastructure, address important but insufficient conditions for SME technology adoption.

Systematic reviews of technology adoption interventions demonstrate that some interventions lead to an increase in technology adoption among firms across manufacturing, services, and agriculture sectors, but these effects are context-specific, as well as intervention-specific.

The most effective interventions combine direct financial support with targeted capability building rather than generic infrastructure expansion.

This insight should fundamentally reshape how we approach digital development. Recent ICTworks analysis of universal internet adoption confirms that most innovators, particularly smaller actors, struggle to navigate regulatory, technical, and financial challenges on their own, as well as to understand and foster the demand-side drivers needed to drive low income end user adoption.

Ecosystem Failures vs. Technology Resistance

The distinction between supply and demand-side interventions matters enormously for policy design.

Research across developing countries confirms that SMEs in developing countries exhibit less enthusiasm for implementing ICT compared to large companies due to financial limitations and organizational and managerial preparedness while the main factors affecting SMEs’ emerging technology adoption are lack of finance and digital literacy.

These are systematic market failures that require targeted interventions.

Ethiopian SMEs exemplify this dynamic. Despite near-universal ICT adoption, enterprises remain constrained by what development economists recognize as classic coordination problems: individual businesses cannot unilaterally solve telecommunications pricing, infrastructure reliability, or skills shortages that require collective action.

Systematic analysis of African SME constraints identifies lack of access to finance, poor infrastructure, and entrepreneurial attitudes are main challenges facing SMEs in sub-Saharan Africa while access to finance remains the largest obstacle for enterprises in the region.

But framing these as individual business shortcomings misses the broader ecosystem dynamics that successful interventions in Bangladesh and India addressed systematically.

Four Demand-Side Interventions That Work

Based on the Ethiopian evidence and comparative international experience, four demand-side interventions offer immediate impact:

1. Professional advisory services at scale.

Rather than generic training programs, establish SME-focused ICT advisory centers offering free professional consulting, tool selection guidance, and implementation support.

The Ethiopian study confirms that while 93% use technology, utilization remains suboptimal due to capability gaps rather than access constraints. Bangladesh’s success stemmed partly from consultatively drawn-up regulatory frameworks and guidelines that provided SMEs with clear implementation pathways.

2. Targeted financial instruments.

Research on SME financial inclusion demonstrates that financial inclusion of SMEs is mainly determined by demand-side factors (ability to manage economic changes, proper record keeping, and willingness to expand the business), supply-side factors (collateral requirements and application procedure), institutional factors.

India’s zero-fee UPI transactions addressed financing constraints directly, while Bangladesh’s mobile financial services created new credit pathways for previously excluded businesses.

3. Coordinated government policy integration.

Ethiopian enterprises identified absence of state support as a universal constraint, while Indian and Bangladeshi success required coordination across multiple ministries and regulatory agencies.

Analysis of national digital ecosystems confirms that effective digital transformation requires enhanced telecommunications infrastructure for sustainable digital growth combined with policy coordination that addresses digital literacy and lack of content in local languages as systemic barriers.

4. Localized technology solutions.

The Ethiopian study’s finding that 32% still consider ICT complex and difficult to use points to design failures rather than user limitations.

Supporting development of local-language interfaces, offline functionality, and low-bandwidth solutions addresses utilization constraints that infrastructure expansion cannot solve.

The post Demand Side Efforts Can Breach 6 Universal Barriers to SME Technology Adoption appeared first on ICTworks.

The WHO Mental Health Atlas 2024 counts a median of 1.1 to 2.4 specialized mental health workers per 100,000 people in low-income and lower-middle-income countries. High-income countries report 67.2, and spend US$65.89 per person against under a dollar.

People in that gap type their problems into chatbots. In a June 2025 study of roughly 4.5 million Claude.ai conversations, Miles McCain and thirteen co-authors at Anthropic found 2.9% were emotional or personal exchanges. The study covered adults only and gave no breakdown by country or language.

That omission is the problem.

The benchmarks deciding whether a model answers a distressed user safely are written in English against North American and European clinical practice. Researchers led by Adrián Arnaiz-Rodríguez at ELLIS Alicante tested leading models against crisis inputs from 12 datasets and found generic and location-inappropriate answers, worst in self-harm scenarios. A helpline number for the wrong country is not a safe response.

$5 Million AI Chatbot Opportunity

Anthropic will award $5 million in individual grants of typically $500,000 to $1.5 million. That funds between three and ten teams to research how AI impacts users’ wellbeing. Grantees also get research API access or credits and occasional technical input from Anthropic staff.

Every funded evaluation must ship as an open-source resource. Anthropic will not veto or embargo findings, and grantees must disclose the funding in all outputs.

Example topics include detecting harmful emotional dependence and measuring whether responses optimize for continued interaction over user interest. The Safeguards team’s guidance lists regional and linguistic variation as a quality criterion, naming slang, coded terms, crisis resources, and cultural norms.

Apply Now: Deadline is 21 September 2026

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