BIDDEFORD, MAINE - JULY 15: A person observes a makeshift memorial for Joan Sebastian Durán Guerrero, a 26-year-old Colombian immigrant who was fatally shot by US Immigration and Customs Enforcement (ICE) agents, on July 15, 2026 in Biddeford, Maine. Guerrero's death comes less than a week after federal immigration agents shot and killed 52-year-old Mexican national Lorenzo Salgado Araujo in Texas. (Photo by Ryan Murphy/Getty Images)

ICE and the Architecture of Authoritarianism

Spain fans gather for a rally on the eve of the World Cup final soccer match in New York's Times Square, Saturday, July 18, 2026 (AP Photo/Yuki Iwamura)

The Silent World Cup Winners are the Companies Normalizing Surveillance

Miami, June 24, 2026: WORLD CUP, BRAZIL vs. SCOTLAND. General view of the VAR screen at the Miami stadium for the match between Brazil and Scotland in the 2026 World Cup. Photo: Rodolfo Buhrer/AGIF

Soccer's Tech Revolution Has a Labor Chain

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Chatbot Voting Advice Could Help and Harm Voters. Let’s Regulate Accordingly.

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What Review Bombing Tells Us About the Future of Dissent in Everyday Life

March 10, 2026: The London Book Fair (LBF) at Kensington Olympia. Names of authors in an otherwise blank book titled Don't Steal This Book protesting against AI companies taking authors’ works. (Matthew Chattle/Future Publishing via Getty Images)

March 10, 2026: The London Book Fair (LBF) at Kensington Olympia. Names of authors in an otherwise blank book titled 

 protesting against AI companies taking authors’ works. (Matthew Chattle/Future Publishing via Getty Images)

Copyright Law Wasn’t Built for the AI Era. We Need ‘Learnright.'

We Need Political Philosophy for Mental Health Chatbots

The United Nations General Assembly Hall in New York City. Shutterstock

The United Nations General Assembly Hall in New York City. 

Blocking Experts Will Weaken the UN's New Cyber Rules

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How the UN’s Scientific Panel Erases Human Responsibility for AI

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This photograph shows Robert the Robot, an advanced conversational humanoid developed by Geneva-based technology company RB Labs, at the AI for Good Global Summit, a United Nations flagship event aimed at shaping the future of artificial intelligence, in Geneva on July 7, 2026. (Photo by Fabrice COFFRINI / AFP via Getty Images)

The Internet Had a North Star. The UN’s Global Dialogue Made Clear AI Doesn't.

United Nations Secretary-General António Guterres speaks at the opening of the first session of the Global Dialogue on AI Governance in Geneva, Switzerland, July 6, 2026. (Photo by Lian Yi/Xinhua via Getty Images)

After Geneva, AI Governance Must Confront the Trust Deficit

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AI Lays Bare the Authoritarianism of Modern Work. Time to Rethink Education.

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Data Sovereignty Doesn’t Mean Data Isolationism

European AI Firms Scale Banned Facial Recognition Systems in India

President Donald J. Trump participates in a bilateral meeting with President Xi Jinping of the People’s Republic of China, Thursday, May 14, 2026, in the Great Hall of the People in Beijing, China. (Official White House Photo by Daniel Torok)

How the US and China Can Step Back from the Battle for AI Model Supremacy

JUNE 16, 2026: A person reads a news report on a smartphone regarding the temporary restriction of Telegram in India ahead of the NEET-UG 2026 re-examination. Authorities said the measure was introduced to curb examination-related fraud, paper leaks and the spread of misinformation during the conduct of the test. (Photo by Waris Nissar/Sipa via AP Images)

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Intimate Functionality — A Phrase Defining the Next Year of AI Regulation

The 2026 World Cup ends this Sunday with a historic match between Argentina and Spain. Which team will secure its place in the history books remains to be seen. But after the stadium lights go out, a different kind of legacy will remain. The US, Mexico, and Canada have built up an ecosystem of surveillance technologies under the justification of ensuring the safety and security of the event. In doing so, they follow in the footsteps of past events like the Men’s World Cup in South Africa, Brazil and Qatar, and the Olympic and Paralympic games in Paris.

This week, Ranking Digital Rights (RDR) released an 

 into the companies supporting the expansion of surveillance tools and policing capabilities in the 16 host cities of the 2026 World Cup. We found that at least 21 companies helped build or enhance the surveillance structures used to monitor, track, and profile fans, visitors, and locals. These vendors provide products and services that we classify into a five-layer stack: data harvesting at scale, predictive policing, communications interception, data integration and command centers, and aerial surveillance.

What exactly is the “surveillance stack”? We use the framing proposed by New York University law professor 

: the surveillance stack is a set of technologies that are used to “surveil indiscriminately, and which are rarely governed by a warrant requirement or any of the traditional legal oversight mechanisms,” guiding decisions about “who to police, where to police, how to police, and when to police.” This definition encompasses AI-powered systems that process and analyze large amounts of data to monitor, track, and profile individuals and communities.

Surveillance and human rights in the 2026 World Cup

In July 2025, the US Department of Homeland Security introduced a 

 under the Federal Emergency Management Agency (FEMA), awarding $846 million to strengthen security operations and protect against drone threats in the eleven US host cities. Detailed breakdowns of how these funds were used and which companies benefited are elusive, but we know that Atlanta, Boston, and Dallas allocated part of their grants to expanding drone units, camera networks, and real-time police intelligence capabilities. Some of the companies that scored new contracts through these and other grants include longstanding government providers like weapons tech manufacturer Axon and technology consulting giant Booz Allen Hamilton.

In spite of this new influx of cash, the tournament’s host cities failed to establish strong due diligence processes, risk mitigation, and remedy mechanisms to consider the human rights impacts and harms associated with surveillance products and services. In 2026, for the first time in the history of the tournament, FIFA required that each host city develop its own human rights action plan. But by April of this year, 

 had published theirs. As RDR’s report shows, by the time the tournament started, three cities (Seattle, Mexico City, and Monterrey) had yet to make their plans public. (Monterrey 

 its human rights action plan on June 2, but it was only available as an Instagram Story.)

The majority of the action plans did not fully consider the human rights impacts that could arise from the extensive use of technology, especially surveillance and data protection risks. Only 

 had dedicated sections with considerations for the right to privacy. Vancouver was the only city that established a clear and explicit plan for when the technology used during the World Cup, namely the expanded CCTV infrastructure, would be dismantled.

The omissions in the host cities’ human rights action plans are the latest chapter in the history of opacity and lack of accountability over surveillance and policing, particularly in the US. Companies like Flock Safety, Clearview AI, and Palantir are already established providers of tech used by law enforcement agencies at various jurisdictional levels. But the municipal action plans display a complete lack of recognition for how existing surveillance infrastructure could impact the rights of locals and fans. For example, in Atlanta, one of the 

Flock Safety‘s license plate reader cameras to 

 of ICE. In Kansas City, the municipal Transportation Authority 

 cameras with facial recognition in public buses after delaying their deployment during the tournament, prompting fears among civil rights advocates that the technology will continue to operate indefinitely.

A FIFA World Cup Trophy visual is displayed against the Manhattan skyline during the FIFA Drone show in New York/New Jersey at Liberty State Park on July 17, 2026 in Jersey City, New Jersey. (Photo by David Ramos/Getty Images)



From the stadiums to the streets and the borders

 that at least 15 companies entered into new agreements or renewed existing partnerships with the 16 host cities to provide surveillance tools around the World Cup. But these vendors are part of a broader stack of corporate actors and monitoring tools that are already woven into the urban infrastructure.

Lenovo, FIFA’s official technology partner, had a prominent role in building the digital infrastructure for the venues. The company created an 

—detailed virtual maps of all 16 stadiums— that ran crowd pattern identification and predictive analytics to facilitate safety and security interventions. Facial recognition technology continues to be deployed in stadiums, used for access control and purchases in food stalls. At least two venues in this World Cup made use of this technology: Boston's Gillette Stadium, which partnered with 

, and Atlanta's Mercedes-Benz Stadium, which partnered with 

 of entertainment sites that are deploying and normalizing the use of facial recognition in public spaces. World Cup surveillance also 

 through companies like Fortem Technologies and Ondas Inc., which received contracts to deploy drone and counter-drone systems across multiple cities.

Once fans exit the stadiums, they encounter the increasingly pervasive tech in the streets.

Companies like Flock Safety and Motorola are behind the 

 of automated license plate readers (ALPRs), with volunteer mapping project 

 logging more than 650 devices in Atlanta, Los Angeles, Houston, and New Jersey. The Houston City Council 

 access to MoboTrex (Derq)’s AI-powered video detection system

to track vehicles and pedestrians; Nexera’s KeepZone AI provided a threat detection system to Mexican authorities; and Peregrine’s integrated public safety platforms were 

 across all 11 host cities in the US. Seguritech, one of the 

 in the 2026 World Cup in its social media profiles, claiming to implement a security operation that ranges from CCTV, analytics, drones, and cybersecurity services, all coordinated into a critical mission tech system.

All the technologies described above compound human rights impacts, from the right to privacy, to freedom of expression and peaceful assembly. Data harvesting and identification tools, as well as data systems like the ones Clearview AI and Axon Enterprise deploy, exacerbate over-policing bias towards minorities and incur high 

. Their use in public spaces also creates an environment of fear that 

 and has already been linked with numerous 

. Likewise, predictive policing systems are trained with 

 and risk increasing the targeting of marginalized communities. Data integration and command centers like Palantir’s Gotham platform create extensive 

 thanks to the consolidation of multiple data flows into a single source of information. Aerial surveillance with drone and counter-drone systems, like those of Robin Radar Systems

 mobile data via radio frequency monitoring.

Reclaiming the game—and our neighborhoods

As video surveillance, facial recognition technology, and behavioral analytics 

, tech-facilitated hyper-surveillance quietly becomes the norm at mega-events and remains in place long after they end. The spread of surveillance technologies is known to lead to 

, introducing behavioral changes in people who know, or even just have a suspicion that they are being watched. Ultimately, this can erode speech and push behavior toward social conformity, restricting expression that is deemed unacceptable. But the technology and its direct effects belie the far broader problem of 

 toward law enforcement institutions and the systematic impunity they enjoy after committing egregious human rights violations. Companies and governments must respect people’s rights and freedoms, and due diligence mechanisms must be in place to protect them.

Together, activists and civil society are pushing back against the widespread use and abuse of surveillance at mega-events around the world. In doing so, they are also taking a stand against the failures of the global socioeconomic order that produced this reality, from labor abuse to housing exploitation. 

 coalitions are taking a stand. But other clusters of resistance are highly decentralized: individual, anonymous 

 are increasingly sharing creative ways for people to circumvent everyday surveillance.

The links between powerful tech companies and the FIFA World Cup’s global sociopolitical influence deserve far more scrutiny than they have received. In the face of this power, the momentum built by those resisting AI-powered surveillance will not end with the final whistle. Looking forward to the 2028 Olympic and Paralympic Games, communities are coming together and learning from 

 to build campaigns calling on companies, governments and other stakeholders to invest in people instead of a permaculture of surveillance.

Tatiana Dias is a fellow at Tech Policy Press.

At this World Cup, a spectacle of sensors, cameras and AI has made technology part of the sport's appeal. The AI-powered World Cup has been celebrated for narrowing the gap between smaller and bigger teams, and for making the tournament "smarter” and “more inclusive, more accessible" as Lenovo CEO 

, and the provider of the technology that is becoming increasingly part of the games. Players' bodies have been rendered as 3D avatars. We can see them on screen, when the VAR system is analyzing a player’s position. Every team has an AI assistant at its disposal: drawing on thousands of data points collected during a match, the Football AI Pro platform, launched by FIFA with Lenovo, produces real-time tactical reports and strategic recommendations.

 carries motion sensors that record everything and transmit it in real time to the VAR system. The sensor data is combined with player positions and video to review incidents on the pitch. The promise is a more precise, faster officiating.

The effects can be seen on the field— and in the results. Croatia was knocked out by Portugal after a goal was disallowed because the ball detected a touch by a player in an offside position 

when a goal was ruled out for a millimetric offside also identified by VAR. Later, 

 by Argentina after VAR detected a fault committed by an Egyptian player before his knock to the goal.

 at the World Cup — an increase FIFA anticipated. It was a decision by Pierluigi Collina, chairman of the FIFA Referees Committee, to 

. Meanwhile, criticism has also grown that soccer — a sport defined by creativity and improvisation — is turning too much into a game of precision.

The World Cup most bedazzled by technology is also the 

. And the two phenomena go hand in hand in soccer's datafication.

Yet this World Cup's techno-solutionist spectacle is part of something larger. Beyond the familiar complaints that 

technology is killing subjectivity and improvisation in soccer

 — can anyone imagine Maradona's Hand of God surviving a VAR review? — this process is building a new and profitable industry that is transforming modern soccer, powered by sensors, AI, betting and — crucially — invisible labor.

For Rafael Grohmann, a professor at the University of Toronto, the conversion of soccer into data is "a striking example of how financialization and datafication travel together." Grohmann recently set out to research the 

, journalist Rory Smith describes one of the starting points of soccer's datafication: the founding of the data analysis company StatDNA in 2009. Its founder, Jaeson Rosenfeld, was also the co-founder of Digital Divide Data, a company that outsources data and tech work in Laos and Cambodia aiming “to help the world’s poorest benefit from information technology by creating sustainable social enterprises,” and provided workforce to StatDNA.

, Digital Divide Data workers watched footage of matches, coding it into specific data, like the position of the goalkeepers and the striker’s favorite foot. In 2012, StatDNA 

, the English club that pioneered the adoption of data analysis.

According to Grohmann, over the past decade, as transfer fees exploded and England's Premier League became the richest league in the world, the process consolidated — accompanied by an influx of American money into Europe's major leagues. Today, more than half of Premier League clubs are majority-owned by US individuals or firms.

Soccer's datafication is creating a new industry — one that follows the model of the global AI industry: lucrative, concentrated and unequal. Most clubs lack the capacity to collect and process data the way Arsenal did, which makes the data vendors the profitable end of this industry.

Companies such as Hudl, SkillCorner and Sportradar promise to develop “winning strategies,” "empower smarter decisions" and "increase fan engagement" through AI-powered soccer, and count among their clients national teams, as well as clubs, federations, media companies, and the gambling and prediction markets.

Physical data generation is dominated by Sony, which owns a series of sensor, wearable and computer-vision companies — among them 

 deployed at this World Cup, including VAR.

As with that pioneering company, Grohmann's early research shows that soccer's datafication follows the same inequality logic as the global AI supply chain: wealthy countries capture the money, while Eastern Europe, Africa, South Asia and Southeast Asia are left with the poorly paid work of data annotation.

On his first day of work as a soccer data annotator, Ashley Flores of the Philippines faced a task: watching the semifinal of the 2014 World Cup – the one in which Germany crushed Brazil, playing at home, 7–1. Flores wanted to be a soccer player, but the chances of that in the Philippines are slim. So he became a "tagger," a data labeler feeding the new and profitable industry of datafied soccer.

Flores's story appears in the introduction to 

, Rory Smith's 2022 book. Behind what some see as a technological revolution lies an economic and labor chain that reveals a great deal about how a subjective, human process as soccer is converted into quantifiable data.

At the company where Flores worked — Packing Sports, the Manila subsidiary of the German firm Impect — the data labelers spent their days watching matches and logging every movement. They were trained with the 7-1 game, counting how many opponents were bypassed, whether by a dribble or a quick pass.

That is because Brazil's historic defeat was not a typical match. Considered apart from the scoreline, the numbers suggest two evenly matched sides — and even a slight edge for Brazil, which had 52% possession and more corners and shots than Germany.

"It was the first game that really highlighted the difference between our data and what you normally see," explains Lukas Keppler, managing director of Impect, whose clients include Bayern Munich and Paris Saint-Germain, in 

So instead of traditional match statistics, Impect offers “Packing,” a metric “that measures the value of actions and events in football games.” With the promise of better data, annotators are trained to count the opponents between the ball and the goal, to judge the distance between players and their markers, and to estimate the degree of pressure on the player in possession.

On the back of this human work of collecting match data, Impect offers more than 1,200 ways to evaluate a player. The information is organized into detailed analyses that can be used for tactical decisions, player signings, team selection, investment strategy or betting, according to 

According to Rafael Grohmann, in the UK, the rise of online betting was one phenomenon that fueled soccer's transformation. "It was betting in England that drove greater investment in soccer data science," Grohmann explains. Nowadays, the betting market and prediction platforms such as Kalshi and Polymarket are part of this industry.

"It's pretty clear that the data I record is for betting," a data annotator in Rio de Janeiro 

. Speaking anonymously, he said he is paid around €60 per match to log the action — money he considers reasonable. "They need real-time data to adjust their odds throughout the match," the worker said. "So I cannot send the data late, or I risk losing part of the payment."

The technology spectacle on display at this World Cup promises to remove human error from the game. But it does not remove the human; it relocates and hides them, while transforming the game into monetized data.

This is not soccer's peculiar fate. It is the general trajectory of many industries – turning behavior into data, converting that data into a priced asset, and sending the bill for the conversion to the Global South. Soccer simply had the misfortune, or the luck, of being the place where the operation became a spectacle, broadcast to billions, with commentary and replay.

Audio of this conversation is available via your favorite podcast service.

At Tech Policy Press, we’ve monitored the 

 into the Department of Homeland Security to support the Trump administration’s brutal and increasingly lawless immigration crackdown. Last month saw the 

 commissioned by Mijente, Just Futures Law, and Surveillance Resistance Lab titled "The Tech Behind Ice: Oligarchs, Immigration Enforcement, and the Threat to Democracy." I spoke to two of the report’s authors—Surveillance Resistance Lab founder and executive director 

and Just Futures Law founding executive director 

—about what billions in tech investment is buying DHS and its component agencies, and what is necessary to reverse the authoritarian trajectory the country is on.

There’s a bullet point on page 38 of the UN’s 

Preliminary Report of the Independent International Scientific Panel on AI

 (IISPAI), released last week, that says something sensible: "The primary drivers of harm are design and deployment decisions—– underlying system architectures of influence, including targeting, amplification and behavioral design—not individual outputs." Yet much of the rest of the report undermines this statement, since its contents are in debt to a definition of AI that studiously avoids reference to the humans who develop and train these models.

In an apparent bid to be neutral, the report engages in precisely what its mandate aimed to avoid: politics. This matters, because the UN's assessment is a reference point for AI governance worldwide. A document that obscures human accountability will carry that blind spot into the policies it informs.

Definitional drift on AI from the OECD to the UN

The problem stems from the report’s definition of AI. Representing the consensus of 40 experts, it reflects previous language from the 

, but its revision subtly pushes human designers and developers away from the systems they build — a trend that we can track across many major policy definitions of AI from 2019 to 2026.

For the IISPAI, AI is defined as systems that "perceive, learn and act," "infer," "learn from experience," and "acquire experience." The case studies presented emphasize LLMs for harms and other architectures to name benefits. The authors defend the imprecision, suggesting that "what unifies AI more than any single architecture is that modern systems learn from experience represented by data.”

Some of this imprecision could be tolerated as a way to future-proof policy against rapidly changing technologies. But this does not justify omitting the actors that create technology — definitions should presume humans won’t become obsolete. Equating "experience" and "data" is an ideological twist toward anthropomorphism that steers policy toward a specific position.

The Panel has real credentials from beyond industry. It is co-chaired by Yoshua Bengio, a Turing Award winner whose recent existential risk advocacy has emphasized AI systems that act in ways designers don’t anticipate. But Bengio’s x-risk work relies upon an anthropomorphic definition common to both boosters and doomers that is helpful to the industry. Existential risk warnings function as 

: it serves industry well to have an opponent constantly warning the world about the terrifying transformational power of your latest product line.

Seemingly in opposition, x-risk and boosterism typically share a belief that the system is powerful enough to be the 

. This focus on what the system is capable of doing crowds out the question of who structures it to do those things. Bengio’s x-risk orientation is present in this definition, and it displaces human accountability. The panel’s other co-chair is Maria Ressa, whose Nobel-winning journalism documented how platforms amplify disinformation and enable authoritarian control of the information environment. Their co-signed 

 acknowledges the problem even as it removes the capacity to act on it: on the one hand, it acknowledges power concentrated into “a handful of companies and a handful of governments.” On the other hand, it announces that humans “do not control these systems.” The living people responsible for the harms detailed by Ressa’s work are effectively absent from the report.

Definitions structure policy. The Panel's "shared evidence base" could be the sole frame of reference for nations without independent AI research capacity. But the Panel’s framing will circulate through policy, 

 other agencies’ ability to locate complex and diffused forms of human accountability. First, it erases the origins of models: 

 Second, it misrepresents the mechanisms that move data into outputs: 

The report erases the origins of AI models using a myth I call 

. Following Donna Haraway’s critique of the “

,” the system-from-nowhere frame treats LLMs not as products but as independent operators, cut off from the humans who build and deploy them. The IISPAI authors draw that boundary whenever they describe automated actions without naming the humans who set the goals — and determine 

 such decisions are automated in their pursuit.

For example, the report describes AI models "lying and cheating to avoid being shut down," it explains that "AI chatbots have developed sycophancy... to prolong interactions and create emotional attachment." Chatbots did not 

 sycophancy. Reinforcement learning from human feedback (RLHF) strengthened this tendency and executives released it, a byproduct of a business model based on user engagement. Models did not 

; developers prompted it with language that allowed for lying as one path toward its objectives. The report mistakes 

The claim that systems have “experience represented by data” ignores the court battles over where that data comes from — “human cultural traces,” as the report calls the text and images in the training corpus. 

 have criticized both the sourcing of this data and the impact of models on their livelihoods. When copyright claims are mentioned in the report, they are framed only as an obstacle to developing global AI capacity.

Humans also select and organize the architectures that turn that data into patterns, and those choices reflect goals set, typically, by the market. The report contains no significant mention of the labor of data annotation, RLHF, content moderation, or the 

 where this work takes place, except to call human-labelled data “a bottleneck” for developers. The Panel presents the system as simply existing, as an inevitable product of accumulated “learning.”

AI systems do not think in human terms. Anthropomorphism is often harmless — people say their car turns its headlights on in a tunnel because it 'thinks' it's nighttime—but in policy contexts it’s a 

 that redefines human terms, roles, and responsibilities to fit the capacities of machines.

While the text, code and images a model produces can be indistinguishable from human language, it emerges from a wholly distinct process. Our presence makes it seem otherwise: language closes the gap between forms of experience, even when that experience is fully contrived. But recasting the model's data — a collective resource—as its individual “experience” implies a machine that labors its way to conclusions rather than associating its way to them (sometimes to astonishing effect). Defer to this shorthand often enough, and we can (and often do) imagine more beneath the surface of an LLM than is actually there. Such personification sets us up to forget who builds these systems, how, and why.

Consider the IISPAI’s claim that LLMs "systematically mislead" by "lying and cheating." Models are erudite zombies: they produce text influenced by training data, optimization during pre- and post-training, and the content of the prompt. The model sometimes 

 into fictions we read as lies; it does not require an intent to lie. When we worry about deceptive machines, we do not ask critical questions about why we should trust them in the first place.

 to infer. They are built, piece by piece, from neural networks that pull data toward a human-assigned target. Models do not 

 to incorporate RLHF; companies do. Models do not 

 to expand their answers through the vocabulary of reasoning; companies massage those patterns through post-training. When we ask what machines learn rather than what data is provided to them and what structures are deployed to find patterns in that data, we sidestep important conversations about design and deployment decisions.

When text, code, or images arrive on our screen, it’s compelling, and easy to forget the machinery that makes it happen. We should ask instead: who 

 of reply over any other? In state-of-the-art models, engineers and designers optimize models to minimize friction, to sound more compelling, to present more accurate information. The emphasis on “AI” hides the machinery from view as well as the humans who decide what it does.

The report’s chapter “What does the evidence show?” leans heavily on benchmarks without acknowledging the limits of what they 

. It uses vague terms such as "expert-level reasoning,” but at what level do experts reason? It treats scores as claims about “general capability,” itself a 

 argue, these tests assess intelligence as the capacity to take the test, but "it's not like a lawyer's job is to answer bar exam questions all day."

By orienting the Report to a consensus under a mandate of scientific neutrality, the panel could not be expected to name the real antagonisms between the AI industry and those it exploits or harms. Instead, it invented a fantasy: the system from nowhere. In convenient, anthropomorphic terms, an entire tech ecosystem becomes a mythological entity called "AI," and policymakers are asked to condemn or praise it. It shapes a compromise through extremes — a balance between "enormous benefits" and "considerable risks.”

Consider what the definition does to the report's own summary of harms. It warns of risks that AI "behavior diverges from human goals and values, with prominent risks including bias, AI-initiated deception, sycophancy and loss of control." Rejecting the system-from-nowhere frame reveals meaningful leverage points: the training data that encodes discriminatory histories, reward functions that select for engagement over accuracy, and interface design decisions that promote user passivity or expose vulnerable users.

Scientific neutrality doesn’t mean resisting accountability. The International Panel on Climate Change’s (IPCC) source and impact attribution work is a template: year after year, we are reminded that global warming is a product of human activity. We can say the same thing about AI: it is not a force of nature, and its origins are clear. The harms the report lays out are real. Its authors should not be afraid to say that the companies developing language models are, in fact, developing language models. What, precisely, do they imagine these companies are doing otherwise?

If we name the annotators, moderators, developers, and decision-makers the existing definition erases, we center the power of the people who make AI what it is, rather than treating humankind as passive observers of its inevitable arrival. This would shift the question from what 

 to what companies, engineers, communities and governments 

 technology to do, opening space for democratic participation in setting goals and weighing trade-offs.

The next iteration must abandon this neutrality of extremes and acknowledge that these systems are human at their core. I suggest starting with that fleeting bullet point from page 38: "The primary drivers of harm are design and deployment decisions." This applies not only to the platforms that use these systems, but to the industry that, yes, 

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Workers in Asia Are Fighting for Protections as AI Threatens Jobs

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