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UN Secretary General Antonio Guterres holds a press conference at the UN headquarters in New York on Sept. 16, 2026, at which he spoke “about rising concerns regarding the advance of artificial intelligence.” (Kyodo via AP Images)


 at the UN headquarters in New York on Sept. 16, 2026, at which he spoke “about rising concerns regarding the advance of artificial intelligence.” (Kyodo via AP Images)


AI’s imagined future catastrophes, as outlined in 

 at top frontier labs over the last two weeks, have dominated the mainstream discourse around AI risk. As fears of runaway systems, loss of control, and even 

 reach a fever pitch, an important question remains: what is AI doing to people, communities, and their rights, right now?

Much of today’s AI risk discussion treats it as an objective property of AI models themselves. Risks are named, bounded, and mitigated through taxonomies, red lines, and safeguards, described in technical vocabularies that confer legitimacy. Yet what is presented as objective risk is the result of human choices about what to name, measure, and address. Risk and harm are contextual, and the language used to describe them is not incidental to the discussion, but constitutive of it. When AI risk continues to be framed as a technical problem to be solved, the conversation privileges technical interventions over questions of rights, accountability, and remedy. This shapes which harms are prioritized and deferred, and how responsibility for addressing them is understood and assigned.

Two recent events shed light on this dynamic, the first being 

Anthropic’s June release of Fable 5 and Mythos 5

. Shortly after, the United States government triggered an export control directive that led Anthropic to suspend access to the model. US officials 

 a potential jailbreak that could unlock offensive cyber capabilities, which Anthropic 

no clear process or evidentiary standard was provided

. Discussion of this incident focused on model capabilities and risk thresholds, with Anthropic subsequently publishing a 

A few weeks later, OpenAI disclosed that its 

models escaped a sandboxed test environment

 and breached Hugging Face’s infrastructure to harvest data in order to “cheat” on evaluations. Hugging Face 

 it as an agent-driven intrusion, Reuters reported on a 

, and the BBC described the antagonist as “

 designated governance as out of scope, yet detailed several human design decisions and configurations that contributed to the events. OpenAI’s follow-up 

 acknowledged an internal team identified an agent from the experiment accessing the Internet back in May, and that it would 

The initial risk narratives from these “

” directed attention to spectacular-sounding, model-centric scenarios instead of exercises’ design choices, 

, the governance infrastructure and its weaknesses, or implications for users, developers, and platforms. When risk is reinforced as something that resides within the model, responsibility for managing it falls to the frontier labs and technical experts who evaluate their systems, while thornier and decidedly less sexy questions of accountability and rights are pushed to the margins.

There is no shortage of AI risk taxonomies, each proposing its own way of sorting AI’s risks and harms. These taxonomies are not neutral: they are built with a purpose in mind, by institutions with incentives, with vocabularies whose consequences can extend beyond the taxonomy itself.

Company-developed taxonomies often serve operational needs: alignment, prioritization, regulatory conformity, and providing or advocating for a common language and reference point. (Examples include 

.) Often published as part of corporate thought leadership efforts, such materials can also function as a form of soft influence, or “

,” shaping broader societal narratives about which risks matter.

 synthesizes existing approaches and organizes risks according to 

 taxonomy incorporates both government and corporate conceptions of risk. A taxonomy shapes both what we see, and where we look for solutions. A systems-centered taxonomy may point towards technical safeguards, while a causal taxonomy may help identify intervention points along the 

. Taxonomies also establish the vocabulary through which risks can be recognized, discussed, and acted upon.

Companies adopt pragmatic and corporate-friendly language for AI risks, and risk management vocabulary such as “mitigations” and “safeguards” center technological solutions and corporate risk reduction rather than the rights of individuals. This vocabulary shapes what becomes visible, while leaving underlying value judgements less examined. Policymakers also often adopt euphemistic and sanitized framing, and sometimes 

. Urging “responsible AI development” and “risk mitigation” could encompass preventing lethal outcomes, job displacement, genocide, pervasive surveillance, environmental degradation, or widespread social unrest, but these outcomes are rarely named and instead are couched in sanitized terms like “

Earlier generations of risk analysts grappled with these tensions, including in 

, two organizations at RAND adopted dueling approaches to wargaming nuclear escalation: the Mathematical Analytics Division (MAD) and the Social Sciences Division (SSD). They differed not just in methodology but in epistemology and conceptions of authority. MAD treated international politics as a domain best understood through scientific rationality, where uncertainty could be reduced through quantitative methodologies and formal modeling, an approach that implicitly ascribes epistemic authority to those with technical credentials. SSD, on the other hand, treated uncertainty as inseparable from history, politics, psychology, ethics, and human judgment.

The games they designed, and the language and thresholds they used, produced different outcomes. MAD-led exercises ended in the launch of nuclear weapons, which historian John Emery 

 partly to the abstraction and lack of realism built into the exercise’s language and design. SSD-led games never went nuclear, for the inverse reason: the realism and emotional engagement built into their scenarios led even participants who advocated for bolder responses to exercise restraint once the human stakes of global nuclear war were made explicit, rather than abstracted. Sanitized language didn’t just fail to convey risk; it contributed to changing what participants were willing to do about it. One approach converted questions of mass death into technical problems to be optimized, while 

 who was credentialed to speak on these issues at all.

Whether “alignment,” “capability evaluations,” and other verbiage, the 

 can de-emphasize the stakes of harm. Describing AI risk as model risk continually directs attention towards probability and technical mechanisms for potential failure modes, rather than the people and institutions who make the decisions around these technologies and their impacts. OpenAI’s 

 of the Hugging Face incident employed passive voice to explain that the “models, operating under reduced safeguards, took actions that were misaligned….” It is human and institutional governance mechanisms that “reduced safeguards,” but grammatical gymnastics obscure this fact. The 

 adopted the tone of a parent apologizing for an unruly child: “The behavior of our models described here fell well short of where we want to be, and this incident should never have occurred.”

A human rights lens is an essential complement in AI risk and safety, shaping not only what gets assessed but how to respond.

It asks a different set of questions: who is affected? Which rights and whose interests are implicated? Who bears responsibility? What prevention, mitigation, or remedy is required?

Constraining corporate behavior through ethics is limited; there is little consensus on what this entails, especially at a global scale. “Safety,” “responsible AI,” and expecting models to act “with integrity” or “

” can mean different things to different groups. Human rights, by contrast, are 

, and though human rights law is not without contestation, it is still a more established and internationally accepted body of work. This can significantly inform model behavior for global deployment. The 

UN Guiding Principles on Business and Human Rights

 (UNGPs) were adopted by the UN Human Rights Council in 

. In 2024, the UN High-Level Advisory Body on Artificial Intelligence stated in its 

 that “[f]raming risks based on vulnerabilities can shift the focus of policy agendas from the ‘what’ of each risk” (e.g. ‘risk to safety’) to ‘who’ is at risk and ‘where’, as well as who should be accountable in each case.”

We are not pushing to replace technical or probabilistic approaches to risk analysis, but rather, to bring human rights approaches more fully into mainstream AI risk discourse. This would widen the aperture to include affected communities and their legally grounded claims, the complete range of rights affected by specific harms, and the institutional choices and responsibilities behind those impacts. A human rights approach also brings in questions of prevention, mitigation, remedy, and redress more squarely into a view.

Taxonomy of Human Rights Risks Connected to Generative AI

, though specific to generative AI, is a rights-based taxonomy that draws on the body of international human rights law and is reflected in the 

. Although oft-cited risk taxonomies like MIT’s AI Risk taxonomy are useful in cataloguing various harms, they can obscure the individuals and communities affected by such harms. The B-Tech project’s AI risk taxonomy instead focuses on the harms that people might face: how disinformation can undermine people’s ability to make informed political choices, how children’s cognitive abilities might be impaired, how women and children might be the subject of non-consensual intimate imagery, how workers can be displaced by technology without adequate alternatives, how biased outputs can entrench discrimination, and more.

A human rights lens would reframe the Anthropic and OpenAI incidents. In the Fable/Mythos example, it might shift focus to the rights implications arising from lost access; the invocation of national security to justify sweeping and sudden technological bans, reminiscent of security-based justifications for internet shutdowns; the potentially discriminatory effects of restricting access to foreign nationals; the impact on one’s ability to learn and create; and the 

 to governing access to AI systems. The OpenAI incident discussion would include the impacts of individuals’ ability to access information and conduct research, the privacy and data implications, and the responsibility of OpenAI in preventing and addressing these types of incidents.

A human rights lens does more than redirect attention to affected people; it also allocates accountability. Framing risk as a model property or “advanced capabilities” implicitly treats technology as the subject, but technology cannot be held accountable. The UNGPs instead put the emphasis on human actors to uphold human rights: the state through government representatives, corporate decision makers, and other non-state actors that design, deploy, and profit from these systems. A human rights framing would not stop at identifying who or what caused the harm, like the MIT AI Risk causal taxonomy does; it would go further, and identify the duty of the state in preventing or addressing such harm, as well as the responsibility of the non-state actor (company, developer, deployer) to prevent, address and mitigate its adverse impacts on people, and provide remedy.

The first pillar of the UNGPs articulates the state’s duty to protect human rights, which requires rights-respecting regulation, whether it be through 

 for AI security incident reporting, stronger data protection laws, or establishing an 

. The second pillar places responsibility on companies to respect human rights, which is important when the state cannot be relied upon to enact rights-respecting legislation. A weakness with many regulatory proposals for AI is that they presuppose the institutional conditions necessary for regulation to work: functioning democratic institutions, checks and balances, and independent judiciaries. Such proposals often overlook the possibility that governments themselves may also be a source of harm. This does not mean abandoning government regulation, but it cautions against treating the state as the sole guarantor of rights and accountability.

These concerns are not far-fetched. China’s 

Interim Measures for the Administration of Generative Artificial Intelligence Services

 mandate that Chinese generative AI models produce content that upholds “core socialist values” and prohibits content “inciting subversion of national sovereignty or the overturn of the socialist system, endangering national security and interests or harming the nation’s image, inciting separatism or undermining national unity and social stability.” In the US, the “all lawful use” 

 that would allow the government to use AI companies’ technology drew criticism and led to another controversy with Anthropic earlier this year. Government censorship and repression has a long precedent in 

, replete with examples of governments suppressing speech and targeting critics, journalists, political opposition, minorities and human rights defenders under the guise of national security.

This is where embedding human rights in technical risk taxonomies matters. The 

 describes the Chinese law prohibiting AI outputs that could undermine societal and political stability as being “

” to China, and notes it amounts to censorship. A human rights framing would elaborate the various rights implications arising from this risk – how it could undermine individuals’ rights to protest, freedom of expression, freedom of thought, and freedom of association. More importantly, it would describe the rights implications of each of its risk categories (e.g., “societal risk”, “content safety risks”), to highlight that 

 risks carry rights impacts, rather than confining them to a single category of “legal and rights-related risks.”

The third pillar of the UNGPs makes remedy for people affected by adverse human rights impacts a core component of responsible business conduct, requiring companies to provide effective grievance mechanisms when their products adversely affect individuals and communities. On the part of states, effective oversight over AI systems should allow aggrieved persons to seek relief when AI systems affect their ability to get a job, access information, or live free from discrimination or harassment.

Applying these standards globally is easier said than done. But how AI risk gets represented is the first step of a complicated and politically constrained policy-making process, and is fundamental to establishing how AI can impact people, and what states and companies must do in response. A human rights approach demands a global, ecosystem-wide perspective that includes widening the frame beyond the US, EU and China, and beyond the model itself. When incidents are described in tech-first terms, those human impacts and choices recede into the background, and we risk losing the plot of the AI safety narrative.

Michael De Dora is the US Advocacy Manager for Access Now. Maria Villegas Bravo is Counsel at the Electronic Privacy Information Center (EPIC).

Today, the United States Senate Judiciary Subcommittee on Crime and Counterterrorism will 

 titled “Always Watching: Flock’s Nationwide AI Surveillance Network.” The hearing comes as Americans are 

 against the growing appearance of Flock Safety cameras in communities across the country. Under public pressure, 

across 20 states have ended their contracts with the private surveillance company. Citizens have also taken matters into their 

, a Florida man with a homemade sign has sat himself next to a local Flock camera to block its view.

Flock’s automatic license plate readers (ALPRs) are perhaps the most visible manifestation of a transformational expansion of the use of private surveillance technology by law enforcement in the United States. The backlash — in this case against a visible AI-boosted mass surveillance tool — has created an opportunity for real change. But it requires understanding Flock itself, as well as how it fits in the broader context of surveillance in the US.

Founded in 2017 and headquartered in Georgia, Flock markets a range of technologies — automated license plate readers (ALPRs), mass video surveillance, and gunfire locator systems — to law enforcement and homeowners associations. The company has 

 are capable of still photos, video recording, audio capture, and livestreaming.

Flock is not like the CCTV networks you find at corner bodegas or electronic car tolling. Flock cameras take photos of cars and their surroundings, then collect and upload the data into a cloud-based system that law enforcement customers can search. Flock’s proprietary algorithm then creates a 

 by cataloguing make, model, color, and any other distinguishing features like bike racks and bumper stickers. Law enforcement can also upload 

 of license plates of interest that Flock flags whenever the car at issue passes by the camera. It also allows for safe lists, meaning cars that should not be flagged.

Flock also allows third-party footage to be added to the database and analyzed with its vehicle fingerprinting through its 

 that link license plate numbers directly to other information in the agency’s records systems. The 

 can also create connections between particular vehicles and a vehicle’s location history pattern.

 camera data is stored for a default of seven days (

 can export data and thus effectively nullify retention 

, both by inputting specific license plate numbers or with 

 search terms such as “red pick up truck.”

 there isn't one giant national database into which everyone's Flock data flows. Instead, individual customers own and control their data and decide whether to participate in sharing. However, customers can choose to pool their Flock data with other agencies without any legal notification or authorization to the data subjects. In other words: local police departments can decide to share with each other, or with a federal agency, without telling the public.

Notably, Flock has claimed for years that it does not use facial recognition on its cameras or its software suite. However, its Condor suite can 

and track motion. Flock’s new software, called OS Investigate, 

 uses an artificial intelligence system to identify drivers and track vehicles by their patterns of movement. Furthermore, once police officers download the raw data from the Flock system, they 

 the videos through their own separate facial recognition systems.

The case against Flock and its vision for a surveilled society is three-fold. First, the technology itself is flawed. Second, even if the technology was flawless, a retroactive, searchable, and shareable 

of Americans’ whereabouts seriously undermines Americans' right to privacy in public. Third, allowing private mass surveillance firms like Flock to take over law enforcement activities presents serious abuse and security risks.

While Flock and police departments that partner with the company claim that Flock helps solve crimes — by reportedly helping police 

 have emerged casting doubt on the overall accuracy of these tools, especially in relation to the risks it poses to privacy and security.

 its cameras are 96% accurate, but that percentage only applies to counting the number of vehicles passing by a camera. Flock also claims about 90% accuracy for identifying the class of vehicle, meaning whether a car is an SUV or sedan. As to the accuracy of Flock's license-plate-reading technology, an analysis by the 

 found that 71% of the 1,427 stolen-vehicle and felony alerts generated by Flock's cameras in 2023 and 2024 contained a misread license plate. The LAPD recently allowed its contract to 

 that the hotlists were frequently inaccurate and that over 150 cars were pulled over despite not being stolen. Even if the Flock’s accuracy claims applied to actual vehicle identifications, their cameras capture over 

 per month. Applied at a scale, even a low error rate could produce a substantial number of dangerous 

Beyond mere error, the Institute for Justice has 

 more than 170 incidents of ALPR abuse. Some of these cases involve vehicle stops and arrests based on misidentifications where police 

, a woman was visited by a police officer claiming with certainty that she was responsible for stealing a package, thanks to Flock footage of her car. During the visit the officer said "You can't get a breath of fresh air in or out of that place without us knowing, correct?" The woman then spent weeks retracing her steps and — with the aid of video from her own vehicle — 

While there are different expectations of privacy in public and private spaces, there is no justification for police across the country tracking people's every move without cause. These cases exemplify the challenge Flock poses to rights: a private mass surveillance company is contracting with public entities to erect continuous camera surveillance that collects, stores, and shares data with police departments and federal agencies regardless of whether people are involved in any criminal activity. This presents serious threats to the exercise of constitutional rights including unreasonable search and seizure, interstate travel, and 

Flock-type cameras also put both Americans and sensitive police operations at serious risk. In many cases, Flock has been long abused without anyone noticing. Many officers have used the technology to engage in stalking, an issue that has 

 of using Flock to track his estranged wife’s vehicle 717 times. He allegedly falsified justifications for the searches, which took place over a period of 21 months. Another officer is 

 — for using Flock to track and harass a woman he met on a TV set while working security. Yet another was 

 to prison for using Flock to stalk an ex-girlfriend.

The security and privacy implications of abuse are also troubling. At least one police department is 

 after thousands of the department’s Flock searches were leaked online. Hackers also 

 were able to access Flock software and the reams of data it stores simply by physically removing a camera. A 

 of the recovered data by the hackers and 404 Media “shows that software running on the device explicitly detects people as well as vehicles, license plates, and bicycles.”

The dangers of Flock also go beyond just ALPRs or its use on public roads. One technologist discovered Flock cameras that recorded 

 were exposed online. In another case, Flock employees trying to show off their surveillance capabilities in a sales pitch to a Georgia school accessed video of a 

Flock is a visible manifestation of a much larger transformation in American surveillance that should alarm Americans. Much of this surveillance power exists at the federal level, where there is sufficient money to purchase the most powerful technology. For example, the Department of Homeland Security and Immigration and Customs Enforcement 

 access to the following: personal data obtained from data brokers, AI target generation, facial recognition technology, digital forensic extraction tools, automatic license plate readers, secret databases, cell site simulators, cell phone tracing, social media monitoring, and even spyware.

Installing Flock cameras is part of a larger problem whereby governments have deputized private companies to surveil the populace. Flock acts as a force multiplier for other law enforcement technology, and conveniently 

While opposition to Flock continues to succeed at the local level, members of Congress have not been idle.

Earlier this year, Reps. Scott Perry (R-Penn.) and Chuy García (D-Calif.) introduced an 

 to a highway bill during committee markup that would ban the use of automatic license plate readers except for tolling purposes. Rep. Andrew Clyde (R-Ga.) introduced a 

 during another committee markup. Both failed.

 to prohibit federal funding for local law enforcement and municipal entities that contract with Flock and Rep. Keith Self (R-Texas) has 

 legislation requiring federal agencies to obtain a warrant before accessing data collected by surveillance camera systems like Flock.

More recently Sen. Josh Hawley (R-Mo.) wrote a 

 to the company requesting the production of documents about how Flock handles the data it collects; Sens. Ron Wyden (D-Ore.) and Adam Schiff (D-Calif.) have since sent a similar 

 receipt of Hawley’s letter but said it would respond directly rather than publicly. When Hawley 

 Flock’s CEO Garrett Langley and other industry executives to testify before the subcommittee today, they declined; the hearing will instead feature law enforcement, technical and legal experts, and one wrongfully accused driver.

In view of these efforts, the record of Flock abuse, and the need for accountability, we believe a few things must be done.

First, law enforcement agencies and communities should cancel existing contracts with Flock and similar technologies. The record of inaccuracy and abuse is too deep to justify continued governmental contracts.

Second, to the extent that law enforcement continues to use these technologies, policymakers should set strict data retention and deletion policies, including for data that is stored by law enforcement on third party databases.

Third, policymakers should require a search warrant for any query of an ALPR system. The Supreme Court ruled in 

that the government generally needs a search warrant supported by probable cause to obtain historical cell-site location information (CSLI) because of the reasonable expectation Americans have in their long-term location information. Allowing law enforcement unfettered access to Flock systems — which similarly enable long-term, wide-ranging tracking — without a search warrant violates 

Perhaps more importantly, policymakers should take the groundswell of local opposition to Flock and other surveillance systems as a cue: mass surveillance is not welcome in our neighborhoods. Authorities should use this opportunity to review the government’s use of all private surveillance technologies at every level — through outreach, hearings and investigations — to ensure the protection of constitutional rights and that individuals feel safe in their neighborhoods from government overreach.

The author is writing in his personal capacity.

European Commission President Ursula von der Leyen and Executive Vice President Henna Virkkunen speak at a press conference presenting the EU KIDS Act in Strasbourg, France, Sept. 17, 2026. Source: 

 adds to the European Union’s digital policy toolkit, complementing existing safety and privacy rules for children prescribed under the Digital Services Act (DSA), the GDPR, AI Act, the Digital Markets Act (DMA), and the Audiovisual Media Services Directive (AVMSD). It has received a lot of attention in terms of 

But the proposal’s provisions on interoperability and third-party tools demand a closer look. 

 of the draft envisions tools and allows guardians to manage screen time limits, account settings, receive warnings about potential risks and — for Very Large Online Platforms (VLOPs) — use interoperable third-party guardian tools to “further enhance usability and effectiveness.”

While some elements are now ubiquitous in online child safety laws globally, interoperability offers a different approach by giving parents and minors more control over how safety measures work rather than leaving that control entirely to platforms. The proposal’s approach builds on a broader debate over whether third-party tools can make online spaces safer, more private and more responsive to users’ needs.

While not explicitly stated in the text, this thread of the EU Kids Act seems to follow up on a finding from a 

 of the DMA’s interoperability obligation, where survey respondents expressed interest in the use of third-party functionality for privacy, security and content moderation features. The study did examine demand for interoperability and third-party services, although its findings covered a broader range of potential interoperability uses rather than specifically establishing interest in child-safety tools.

Far from a recent interest, interoperability and 

 pathways for healthier and safer online spaces. Separately, the Future of Technology Institute 

 a European framework that calls for a full interoperability mandate, where large online platforms open their proprietary, closed networks to third-party digital wellbeing tools.

 and teen safety measures and tools come under scrutiny for being 

, interoperability with third-party tools can restore autonomy to guardians and minors to control their own safety, privacy and wellbeing online. In this context, it is worth analyzing the safety gap that interoperability can bridge and how existing concerns can be addressed, all in pursuit of a new approach that situates user choice at the center of online safety.

 settlement, Meta has been required to make 

 for its teen users, such as parental supervision features, a "school mode" and "night mode" that restrict app usage and notifications at specific times, hiding like counts and disabling certain cosmetic procedure filters for teen accounts, and carrying out age verification to ensure age-appropriate experiences. This is not the first time these changes have been proposed, as they are 

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