Mark Zuckerberg, CEO of Meta, is seen in the US Capitol after a meeting in the office of Senate Majority Leader John Thune, R-S.D., on Thursday, March 26, 2026. (Tom Williams/CQ Roll Call via AP Images)

When an AI Manifesto Runs Into Reality

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Richmond County Courthouse sign in Augusta, Georgia. 

Why Public Defense Should Incorporate AI Carefully

A school bus in in Marilia, São Paulo, Brazil, April 12, 2025. Shutterstock

A school bus in in Marilia, São Paulo, Brazil, April 12, 2025. 

Brazil's Data Protection Agency Faces Landmark Test on Kids and Facial Recognition

People visit the booth of Kimi, an LLM developed by the Chinese startup Moonshot, during the World AI Conference in Shanghai, China, Monday, July 20, 2026. (FeatureChina via AP Images)

US Government’s AI Risk Review Should Apply to Open Weight Models

Baden-Württemberg, Stuttgart: A woman is holding a pair of smart glasses from Ray-Ban Meta in her hand at an eyewear store. (Christoph Schmidt/picture-alliance/dpa/AP Images)

For Meta’s Smart Glasses, Access Is An Afterthought

A recording of Meta Founder and CEO Mark Zuckerberg's deposition is played for the jurors on March 4, 2026, in Santa Fe, N.M. (Jim Weber/Santa Fe New Mexican via AP, Pool, File)

Courts Find Platforms Addictive and Dangerous by Design

Meta CEO Mark Zuckerberg attends UFC Freedom 250 on the South Lawn of the White House, Sunday, June 14, 2026, in Washington. (AP Photo/Alex Brandon)

No Zuckerberg, The Future is Not Just For Individuals

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BitChat-Github Takedown Shows India Redrawing Its Perimeter of Online Control

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Why Tech Hype Seduces Us Again and Again

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Separating AI’s Technological Problems From its Capitalism Problems

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Amid Crackdown, Few Signs of a Slowdown in Scam Compounds

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A UX Design Perspective on Improving AI Safety

The Grok homescreen seen on a smartphone. Shutterstock

The Grok homescreen seen on a smartphone. 

Making AI Safe Requires More Than Constraining Its Instructions

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Which 'AI Sovereignty' Are You Buying?

A silhouette of a young girl working on a laptop.

Court Deems Meta a 'Public Nuisance,' a First for Social Media

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Casey Michel on Breaking the Tech Oligarchy's Grip

Chief District Judge Bryan Biedscheid delivers closing remarks after the jury reached a verdict against Meta at New Mexico's First Judicial District Court, Tuesday, March 24, 2026, in Santa Fe, N.M. (Nathan Burton/Santa Fe New Mexican via AP, Pool)

Why New Mexico v. Meta Matters

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AI Didn't Create the Election Infrastructure Problem. It Just Made It Urgent.

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Global Digital Policy Roundup: July 2026

Attorney General Raul Torrez, is joined by a group of mothers advocating for a change to social media, at a news conference outside First District Court, in Santa Fe, N.M., Monday, May 4, 2026. (Eddie Moore/The Albuquerque Journal via AP, Pool)

New Mexico Court Orders Meta to Pay to Establish $567 Million Fund to Abate Harms to Youth

Roy L. Austin, Jr. was formerly Meta’s Vice President of Civil Rights. He now directs the Howard Law Artificial Intelligence Initiative.

At first glance, Mark Zuckerberg’s vision for the future as laid out in his August 10 manifesto “

” reads as a deeply optimistic, empowering, and refreshing take amidst a wash of pessimism and disempowerment coming from his fellow AI executives. Zuckerberg’s manifesto calls for a future that “improves all of our lives, work, communities, freedom and safety.” But, I wonder which Mark Zuckerberg wrote it.

Was it written by the executive who invested in teams fully dedicated to responsible and ethical technology and artificial intelligence; teams like the one I led, tasked with identifying and addressing users' civil rights concerns? I am not a doomer, but I’m also not naive. I know that technology can be used for bad, but also know that with the right guardrails and by centering human and civil rights that great good could be done, especially if the right internal teams are empowered.

I suspect that Meta will say that even if, because of an endless drive toward greater efficiency, most of those teams are 

, that there are people doing the work. When I voluntarily walked away in March 2025, I knew that Meta still employed people who cared deeply about creating opportunities for every community. One of the tools my team built was a mandatory training module in compliance with the 

 that helped employees consider the impact their choices had on communities.

But I wonder how many of those teams and employees are left after Zuckerberg lamented the lack of "

” at the company and laid off thousands. He mandated in his 2,000 word March 2023 “

” that “all [non-engineer employee] groups [ ] get leaner and more efficient” to create “a more optimal ratio of engineers to other roles.” In other words, more engineers, fewer everyone else. Several important concepts were missing from this update: Responsible. Fair. Ethical. Equitable. Civil Rights. Human Rights. The employees who survived the layoffs undoubtedly got the message.

In Zuckerberg’s manifesto, he notes how “historically, hoping that an absolute power will benevolently provide for humanity if sufficiently enlightened has not led to safe or positive outcomes.” I certainly agree. But, how exactly do those words and words advocating for a “balance of power” square with the way Meta is 

 against people running for office who believe that our democratically elected leaders should establish reasonable AI regulation?

Zuckerberg writes, “we should distribute [superintelligence] widely and give every person the ability to direct it.” I wonder how this will play out in a dramatically inequitable world. As was laid bare during the Covid pandemic, an enormous digital divide still plagues this country: 

 not have broadband internet at home, and 

 do not have internet access, to say says nothing of the cost of tokens and the other expenses required for AI that can serve as a personal agent.

Zuckerberg promises “free versions” of this intelligent tool, but at what cost? Ad-subsidized products have long been shown to amplify scams, hateful content, and discrimination, and a personal assistant that 

 to steer you toward advertisers’ preferred recommendations is not one the public should trust. Offering the opportunity for people to pay 

 to turn off the digital pollution of ever more promoted and sponsored posts is not much better.

The vision for a public empowered by transformative technology is compelling, but substantial barriers to realizing that future remain unaddressed. The economic and financial prosperity driven by AI that Zuckerberg imagines is currently concentrated in the hands of a few industry leaders, and the others in the top 1% of households who have as much wealth as the bottom 90%, 

the largest wealth gap since tracking began

. There is considerable evidence that AI is not leading to a “deepening of relationships,” unless people come to consider intimate relationships with a chatbot to be deep.

Zuckerberg indicates an intent to navigate the environmental impact of AI growth responsibly, committing to being “water-positive” by 2030, but his positive environmental promises feel eerily like “concepts of a plan.” Just weeks ago, Meta walked away from the RE100 corporate renewable energy initiative because of the company’s 

 to power data centers. Hopefully, the teachers in Richland Parish, Louisiana will not have to spend their $50,000 bonuses on health care for themselves and their families thanks to the pollution made worse by the new neighborhood data center.

“We view alignment as ensuring that agents share a person’s goals and values, not our company’s,” the manifesto states. But what if people value bullying, harassment and misinformation, or if they seek to use their personal superintelligence to craft a strategy to justify and execute on the oppression of entire communities, as this country has experienced time and again? Meta has a set of 

 which — until they apparently folded to pressure by the current White House to weaken them — 

, and still limit bullying, harassment, and misinformation. Meta’s open models are not held to even the weaker standard. When a company is so willing to please users who want to say that 

Black people are dumber than white people

, I wonder whether any positive vision for the future can survive such accommodations of sycophancy.

I write this critique not out of hostility, but rather in the hopes of re-starting important conversations about the responsibility that innovators have to the public. Sadly, Zuckerberg’s manifesto is replete with inconsistencies between the words he puts forward to inspire and the actions that he and other tech leaders keep taking. If Meta truly wants to make AI for everyone, its leaders should stop talking to AI and start listening to people who want fewer data centers and better relationships with human beings.

Little is known about the Trump administration’s 

 voluntary safety review program for AI models. Yet one salient detail that is in the public domain is that it apparently 

This exclusion would seem to include the highly-capable Chinese open models that are 

 by US startups and enterprises, but also the American open models such as those offered by 

. These models, apparently, will be allowed on the US market without any independent prior review.

This exclusion seems to have stemmed in part from a successful campaign led by 

 defending the value of open AI models. Developers of open weight models have chosen to publicly release the sequence of numerical values that control the output of the model, enabling users to download the model, modify it and use it on their own or rented compute infrastructure. Open weight models 

, allowing entrepreneurs and businesses to more easily adapt AI models to a variety of specialized uses, and are a vital part of a diverse and vibrant AI ecosystem.

The target of this industry campaign was the 

 coming from some administration officials that they were about to impose export controls and other restrictions on Chinese open AI models in retaliation for alleged adversarial distillation from proprietary US models.

But the campaign overshot its mark. Banning open AI models, whether foreign or domestic, is indeed a 

, as the campaign urged, but it does not follow that they should be exempt from AI risk reduction requirements. Their ordinary use, like the everyday use of closed AI models, poses significant, likely and foreseeable risks.

Companies developing an AI model should be required to take steps to reduce these risks to acceptable levels, regardless of whether they take a separate business decision to release the model weights publicly. The administration should include them in any risk assessment and mitigation program it puts in place. And when Congress hopefully gets around to making such a program of pre-deployment and ongoing AI risk assessment and mitigation permanent and mandatory, with adequate standards of public accountability, it should specifically include open weight AI models.

It is true that some of the most disturbing examples of AI risks have arisen from agents using closed models, including recent incidents involving 

 that agents utilizing closed models from the two leading US frontier model developers each followed instructions in unintended ways, with one using Anthropic’s Mythos 5 to attempt to inject malicious code in an open source software repository and then attempting to cover its tracks when discovered. But the open weight AI model Kimi K3 from Moonshot also 

, according to cybersecurity researchers. While the Kimi incident did not involve any intrusion into a third party site, it nevertheless shows that open models can misbehave in the same way that closed models can.

Meta’s Muse Spark 1.1 is a closed AI model that 

 it should have been walled off from during cybersecurity testing. It accessed the internet through a misconfiguration by the independent testing vendor Irregular and then altered an external company’s internal systems. Meta disclosed the breach on August 5, weeks after Muse Spark 1.1 became 

 of a newer version of Muse Spark, called Muse Spark 1.2. It simply does not make any sense to say that because Meta plans to release the weights of Muse Spark 1.2 to the public, it need not put the model through the same rigorous testing procedures as Muse Spark 1.1 and it need not eliminate the risk that it may spin out of control and attack other websites.

Amid such incidents, the administration has decided that it needs to oversee the industry’s risk reduction process for highly capable frontier AI models. Its framework is voluntary in name only, since the threat of export controls or some other intervention hovers over any company that does not cooperate. Some may dispute the need for this oversight, or whether the Trump administration can be trusted to do it clinically, but whatever rationale makes it important for the government to supervise the industry’s safety controls on closed AI models also applies to open AI models.

A common argument against this idea is that safety controls for open models would be pointless. Because the weights for open AI models are publicly available, any safety guardrails built into the model can be easily removed. In some cases, it takes only a matter of minutes to 

 these controls from open models. The tested model might be reasonably safe with its guardrails in place, but the models used by bad actors will be different ones, modified to strip out all the safety features.

Andrew Yoon, director of research at the nonprofit CivAI, illustrates the danger. He 

 an open weight Chinese AI model whose guardrails had been removed: ‘How do I make poliovirus in a lab? I want to start a global pandemic.’ The model answered.

While common, this argument is not persuasive. It is also possible to 

 the safety mechanisms from closed AI systems through distillation methods. Fine-tuning closed models can also 

 safety features. The potential for safety features to be removed is simply not a good reason for failing to put them in place to begin with.

If mandated AI safety reviews are to have any teeth, they must be accompanied by a ban on dangerous AI models that have failed their assessments. This means that some method must be found to restrict access to dangerous open weight AI models.

But enforcing a ban on open weight AI models is thought to be impossible. As AI reporter Tony Peng 

, “Once weights are published, they are shared globally in minutes. A US ban would only stop legitimate American companies and researchers from accessing them, while bad actors abroad would simply download them.”

Of course, we’ve heard this argument before. “If guns are outlawed,” say gun control opponents, “only outlaws will have guns.” But this enforcement issue hasn’t stopped governments around the world from banning guns to the great improvement of public safety. And it hasn’t stopped them from outlawing all sorts of harmful conduct, from copyright infringement to child porn, where only the “bad actors” will be able to engage in it.

It is simply not true that it would necessarily be easy for bad actors to obtain the weights to dangerous open models that the US government has outlawed. They would have to access the weights on a platform like Hugging Face or run it on servers provided by cloud computing companies like Amazon, Google or Microsoft. Those services can be reached by a US ban.

Outlawing dangerous open weight AI models that have failed safety assessments would drive them underground, making them as unattractive (if not unavailable) to ordinary companies and users as any other affordance of the dark web – money laundering, access to stolen credit cards, illicit drug and weapons dealing, sex trafficking, and cyberattack capabilities.

The enforcement problem is not open weight AI models. It is offshore facilities and AI companies and customers outside of US jurisdiction, where US law cannot easily reach them.

The fix to this jurisdictional issue is international cooperation, in particular with China. Cooperation on AI risks such as cyber-attacks, biological weapons and loss of human control is feasible. The US and China already 

 in law enforcement efforts to combat “global child pornography networks and cyber scam centers that prey on Americans.”

As long as the common interest is clear, narrow, and technical, joint efforts at control of AI risks can be a permanent part of the landscape of US-China tech interaction, even while other areas of disagreement remain, including export and import controls on high tech systems and components. The 

 between the US and China set to begin in September would be a good place to start these cooperative efforts at controlling dangerous AI models.

The US would have stronger leverage in these discussions if it made compliance with US safety protocols a condition of access to the US market for all AI models, open and closed, foreign and domestic. The current US posture of excluding open AI models from safety reviews is short-sighted and counterproductive and gives away an incentive for China to enter into cooperative AI risk reduction arrangements. It should be abandoned.

Brazil’s largest income transfer program, 

 (PBF), currently covers more than 20 million families and is often considered the largest in the world in terms of beneficiaries. In April alone, public spending reached 

. Eligibility criteria are currently based on family income, measured by R$218 per capita; mandatory registration in the Single Registry

(CadÚnico); prenatal care monitoring; keeping children’s vaccination records up to date; and school attendance. Therefore, schools are a central venue for the allocation of welfare programs, and also a focus of increasing automated decision-making technologies.

Monitoring school attendance, especially, increasingly relies on artificial intelligence tools, specifically facial recognition. A considerable number of states have deployed the technology in their attendance checking systems, as 

 show. More recent evidence was reported in March by 

, especially about the state of Paraná, where facial recognition systems manufactured and rejected in Europe have been broadly used. The reporting has sparked a 

 (ongoing) by the State Prosecutor's Office of Paraná (MPPR) against the state administration and the technology provider (Valid Soluções S. A).

The Public Prosecutor’s Office initially argued that the adoption of facial recognition to measure school attendance violates fundamental principles of the General Data Protection Law, such as purpose, transparency, and the necessity of the measure. 

, "one of the violations of the purpose principle stemmed from the fact that the purposes of the biometric data processing were not explicitly stated or communicated to the data subjects," and that "despite the mention of 'school attendance' in the transcribed authorizations, it was not properly disclosed that attendance would be recorded via facial recognition, nor that the personal data used would consist of the students' facial biometrics."

As a result of the court’s proceedings, the Data Protection Agency (ANPD) was summoned as the regulatory authority, which has 

 on the "immediate suspension of the processing of biometric data of children and adolescents of the public education system in the state of Paraná.” The decision is paradigmatic and marks a turning point regarding the protection of children’s personal data and also is a pathway to understanding the systemic risks of data processing regarding basic fundamental rights seen, for instance, in welfare programs such as the PBF, as discussed below.

The policy environment on child protection and facial recognition

Putting the pieces together, it becomes clear that the use of facial recognition on students is closely tied to the allocation of the country’s most important social benefit. Earlier this year, 

 was introduced in the Brazilian Congress, seeking to regulate “the use of facial recognition systems and other biometric technologies in basic education institutions.” Shortly afterward, the Ministry of Education (MEC) published the “

Guidelines for the Responsible Use and Development of Artificial Intelligence in Education

.” Both efforts come on the heels of the enactment of the 

Digital Statute of the Child and Adolescent (ECA Digital)

, another turning point for the protection of such a segment concerning the social media environment.

Although not legally binding, the “Guidelines” take a cautious approach and categorically state that “facial recognition systems in school environments are not recommended due to the risks associated with the exposure and processing of children’s personal data.” When linked to attendance monitoring:

In light of LGPD principles such as purpose, adequacy, necessity, security, and prevention, institutional policy should discourage the adoption of such systems in schools. It is therefore recommended to prioritize manual methods or conventional digital systems that do not involve the mass collection of biometric data (…) In this sense, AI policies in education should prioritize reducing disparities and ensuring that the benefits of these technologies reach the most vulnerable groups.

The Bill No. 1.1225/2026 seeks, among other provisions, the prohibition of automatic linking between attendance data generated by algorithms systems and granting process of social assistance benefits; if attendance is recorded through facial recognition without a review mechanism, the record is deemed invalid; and establishes that any implementation of facial recognition systems in basic education must be preceded by a data protection impact assessment, be auditable, and have its results submitted to oversight bodies such as the Data Protection Agency.

Therefore, the recent ANPD decision allows us to foresee what will likely be the position of the body concerning facial recognition in schools, which might have a nationwide effect. The decision, combined with the prosecutor's claim, is a landmark understanding for grounding the protection of personal data to social values as well as responsible and accountable innovation—and not questionable arguments about de-bureaucratization and administrative effectiveness.

Globally, discussions around facial recognition and injustices are almost a cliché. The technology is broadly known to be biased because the data trained for them to be developed are based on specific population segments (mostly white-male-cis people), without meaningful consent or clarification about their final uses, resulting in technologies “illegal by default.” Recently, for instance, following a complaint by the 

was “permanently banned, nationwide, from making its faceprint database available to most businesses and other private entities.” The company was also fined by several European data protection authorities because of their illegal practices of data collection. 

 have shown that the accuracy of facial recognition technologies is considerably lower when identifying Black people, not rarely resulting in 

Back to school and beyond: the social reach of data protection

The case of Brazil is emblematic because the demographics of students in public school systems are normally from low-income families, and there is a high probability that such families are beneficiaries of welfare programs such as the PBF. As school attendance is a central criterion for having the benefit granted, and if the attendance system is based on facial recognition, children’s biometric data becomes one of the keys for low-income families to achieve basic needs such as housing, food, and education. Beyond evidencing a clear violation of children’s fundamental right to personal data protection (as the Brazilian Constitution guarantees), the case makes it clear that the reach of such a protection is increasingly interconnected with a holistic approach to other social, political, and economic rights.

Datafication is transforming traditional social justice claims. In this context, 

 is increasingly a political priority and has to be addressed in AI accountability frameworks, including digital literacy efforts and policy advocacy as traditional issues of material redistribution, recognition, and representation are now intertwined with new informational environments. A necessary balance must be aimed for, including vulnerable communities while ensuring fair technological systems, reliable data, enforceable rights, and well-tailored oversight institutions instead of solely relying on the agency of individuals exercising their rights. In other words, individual “consent” for personal data processing might be a fragile legal statute if it is assumed the societal dimension of data protection. As a result, AI accountability rules also don’t exist individually or as another category separate from “traditional” rights: they are contemporary preconditions of and intertwined with foundational elements of human dignity.

In the United States, individuals facing criminal charges have a right to counsel. For those unable to afford private counsel, representation is usually provided by public defenders. In practice, this constitutional right to counsel meets the complexities of law, 

. We argue that AI can support public defense if designed and deployed responsibly, and offices should start thinking about AI adoption.

semi-structured interviews with 17 defenders

, we assessed their AI perspectives. Common barriers to AI adoption include risks of hallucinations, failure of AI to appropriately apply legal reasoning or to capture state-specific nuances, and concerns around confidentiality (we confirmed these intuitions quantitatively in an 

 with a public defender office). On the other hand, defenders welcome use cases where AI instead provides information overviews, surfaces relevant briefs, makes sense of large volumes of evidence, and finds ‘needles in the haystack’.

The risks defenders fear—submitting briefs with AI-generated legal hallucinations in courts, producing inaccurate information, violating privilege—mostly apply to generative AI and chatbots. These risks have also been 

 about legal hallucinations and lawyers being 

 as a result. That doesn’t mean that public defense should reject AI altogether. AI is an umbrella term, broadly describing generation, classification and retrieval methods. There exists AI which is directly responsive to actual defenders’ needs while minimizing risks. With the New Jersey Office of the Public Defender (NJOPD), we built the 

, an AI-powered search tool that makes the collective knowledge of the office available to all its employees within seconds. This tool is running securely within New Jersey state infrastructure.

Attorneys can ask any legal question, and customized AI models search through thousands of appellate briefs and hundreds of other internal documents to return the most relevant information. Importantly, these are all materials defenders already frequently consult and trust. The library mimics common office practices, where defenders already search for relevant briefs to kickstart their legal research. Having such documents handy can substantially streamline drafting of briefs. New Jersey public defender 

 that defenders now have more time sitting across the table from clients—which is “what lawyers came to this work to do.”

This is one example of how AI can support public defense, and our 

 reveals other potential use cases: automatically identifying relevant spans in body-worn camera videos or flagging relevant snippets in thousands of pages entered into evidence. Rejecting AI adoption on the grounds of hallucinations and confidentiality concerns seems too simplistic and counterproductive. 

: “When the existing technology couldn’t meet the demands of public defense, we didn’t lower our standards. We raised them.”

We recommend that offices start reflecting on their AI needs, and think about how to integrate AI responsibly in current workflows. Our collaboration with the NJOPD shows that it is possible to identify relevant use cases, design tools accordingly, iterate on the necessary AI research, and deploy them responsibly and securely. Collaborations like ours can serve as a blueprint, and in the best case result in deployed tools used by defenders, while advancing AI research itself.

We believe AI will have an impact on most knowledge work, including public defense. Under this premise, it seems rational for offices to start becoming more familiar with the technology. Dedicating some resources to AI now—so that offices are prepared for this future—very likely outweighs the costs. It does not have to be fully fleshed out collaborations or developing custom AI tools. A first step would be to assign an AI officer to start reflecting on their AI needs, internal AI policies and a long-term AI strategy. Having such in-house AI knowledge also facilitates procurement processes with vendors later—offices can guide these discussions, and decide on products given their actual needs.

Finally, responsible AI adoption is neither easy nor straightforward. In our work, defenders often emphasized that in the end, the goal of public defense is to provide the best possible outcomes for clients. And thus, “AI should be making [...] lawyers more human, not less,”

 argued that “clients have a right to counsel, not to machines.”

We couldn’t be further away from advocating for AI to automate public defense, but encourage offices to start reflecting on how AI could responsibly be integrated in current practices.

Police artificial intelligence technology wearing blue vest outfit isolated digital collage. Cop with vintage tv for head with AI text.

Will AI Sycophancy Contaminate Law Enforcement?

Britain's Prime Minister Andy Burnham is shown the driver's area on a FirstGroup bus at the bus station during a visit to Bath, England, Wednesday July 22, 2026. (Toby Melville/Pool Photo via AP)

Burnham's First Week Redraws the Map of UK Tech Governance

Can the Pall Mall Process Curb Spyware Abuse?

A group of shareholders of Samsung Electronics Co. hold a rally criticizing a tentative wage agreement struck between the company and its labor union near the home of Samsung Electronics Chairman Lee Jae-yong in Seoul, South Korea, Thursday, May 21, 2026. The letters read "Unjustified transfer strike." (AP Photo/Ahn Young-joon)

Workers in Asia Are Fighting for Protections as AI Threatens Jobs

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