Stefaan Verhulst

Report by CRS: “As automated tools for decisionmaking, including those using artificial intelligence (AI), have grown in popularity and sophistication, they raise questions about how their use intersects with federal antidiscrimination laws. Federal laws address various kinds of discrimination, including disparate treatment and disparate impact.

Disparate treatment generally occurs when a person makes a decision or classifies people on the basis of a protected trait, such as race, sex, religion, or another characteristic. Int’l Bhd. of Teamsters v. United States, 431 U.S. 324, 335 n.15 (1977).

Disparate impact occurs when a person makes a decision using neutral criteria, but the decision adversely and disproportionately affects members of a protected group, and either the decisionmaker cannot put forth a legally adequate justification for using the criteria, or an alternative exists that would serve the decisionmaker’s purposes with less of a disparate impact. Tex. Dep’t of Hous. & Cmty. Affs. v. Inclusive Cmtys. Project, Inc., 576 U.S. 519, 524–25 (2015).

How disparate treatment and disparate impact frameworks apply to decisionmaking via automated, algorithmic tools (including AI) is not always clear. Some situations involving algorithmic decisionmaking would clearly be disparate treatment under present law. For example, a person covered by antidiscrimination law who intentionally designs an algorithm to discriminate on the basis of a protected characteristic, or who selects an algorithm because of its discriminatory effect on a protected group, engages in disparate treatment. See Pers. Adm’r of Mass. v. Feeney, 442 U.S. 256, 279 (1979).

However, the law is unsettled on how to evaluate cases where no human consciously chooses to discriminate, and yet an automated system classifies people on the basis of protected characteristics. There is also substantial uncertainty over when an algorithmic system could be said to engage in disparate treatment via proxy—that is, classification based on a facially neutral trait that is closely correlated with a protected characteristic…(More)”.

Book edited by Trace Gale, Andrew Curry, Sue Robinson, and Sahar Hadidimoud: “…offers the first comprehensive synthesis of the Three Horizon approach—a participatory approach first introduced by Anthony Hodgson and colleagues, for understanding how change happens in complex systems, envisioning possible and desired futures, and navigating the paths between. The framework provides a versatile scaffolding for navigating complex transitions that translates readily across disciplines, sectors, and worldviews, enabling collaboration across difference—what scholars call a “boundary object”.  It reveals how current systems (Horizon 1) lose fit over time, how alternative possibilities (Horizon 3) emerge from the margins, and how the turbulent space between (Horizon 2) becomes the site of transformative change. 

This edited collection offers a comprehensive synthesis of Three Horizons theory and practice, weaving together contributions from the framework’s co-originators with global case studies, practical tips, and tools. It moves from conceptual foundations through applied practice to deeper questions of perception, hope, and transformative capacity—exploring collective sense-making, strategic reflection, and systemic learning. In a world navigating polycrisis—from climate change and technological disruption to social transformation—Three Horizons approach offers a rare integration of systemic thinking and creative imagination.  This book makes that integration accessible and actionable, an essential resource for academics and practitioners in possibility studies, future and anticipation studies, education, sustainability and environmental sciences, and social change…(More)”

Book edited by V. Joseph Hotz, Ruobin Gong, and Ian M. Schmutte: “… collection of new research findings and ideas that shed light on the current movement to advance data privacy protection methods and the impact these innovations have on the conduct of empirical analysis in economics, computer science and statistics using privacy-protected data. Its chapters provide short introductions and overviews of key issues related to privacy protection of data used in social science research, including outlines of new frameworks for characterizing and assessing privacy protection of data, discussion of challenges for disclosure limitation in the context of survey data and new designs to improve access by researchers and the general public to confidential data…(More)”.

Policy Primer from the Australian Academy of Health and Medical Sciences (AAHMS): Explores the meaning and purpose of horizon scanning, and some of the different shapes this can take; Maps the Australian health and medical science
horizon scanning landscape; Overviews key international approaches to and national systems for health and medical
science-related horizon scanning.
This primer is intended to inform discussion across government, research funders and the broader health and medical sciences (HMS) sector about the role and design of a nationally coordinated HMS horizon scanning capability.
It maps key past and present Australian approaches, as well as some existing international approaches, and identifies questions that could provide a starting point for exploring next steps that Australia could take towards realising a nationally coordinated horizon scanning model…(More)”.

Paper by Janosch Pfeffer et al: “Climate citizens’ assemblies convene randomly selected citizens who, after structured learning and deliberation, develop policy recommendations. Yet, whether they produce more ambitious recommendations and influence political decisions remains contested. Here we conduct a comparative, counterfactual assessment of 488 recommendations from five national and one state-level assembly in Western Europe, drawing on document analysis and expert interviews. The results show that citizens consistently proposed more ambitious climate measures than existing government policy. Recommendations sometimes influenced policy by adding momentum, enriching plans, breaking deadlocks or introducing novel ideas, but such influence was rare. Only the French assembly shows substantial policy impact, with one-third of its recommendations having influenced policy, due to strong power delegation, political leadership and resulting public pressure. In the other cases, policy impact was minimal or absent. These findings show that citizens’ assemblies can raise policy ambition, but translating recommendations into political decisions depends strongly on political conditions…(More)”.

Article by Zoe Baird: “The leaders of the major frontier AI developers are sounding the alarm at the speed at which AI is creating potential catastrophic risk, calling for a slowdown and government regulation. But Congress and the president are far from consensus on government’s role. Congress is unprepared and needs to devote serious attention to legislative options. As it does, it should quickly establish a bipartisan public-private AI Commission to define solutions for responding to public alarm and industry warnings.

There is precedent for using a Commission to build rapid consensus in response to a radical change in the threats at hand. The end of the Cold War was one such moment. The terrorist attack of September 11 was another. In each case, the existing machinery of the US government had been built for a world that no longer existed. The moment required a clear-eyed rethinking of government’s role itself, at a speed government rarely manages.

We are in such a moment now. The people building artificial intelligence — the engineers, researchers, and executives who understand the technology best — are the ones sounding the loudest alarms. That is not normal. Industries do not typically lobby for their own regulation or warn that their products could cause catastrophic harm. When this happens, the debate about whether government needs to act should be over. What remains are the hard questions: what should government’s role be, and how does it organize itself to protect the AI future while maintaining America’s lead in AI development?

Certain steps should be taken right away, like requiring embedded independent reviewers and government safety review, but for Congress to frame regulation it should establish an AI Commission and give it six months to make its initial report. This will provide a thoughtful space where points of view can be explored deeply and participants can learn from each other. Its membership should include congressional leaders of committees for technology, commerce, and national security; former government officials; public interest and private sector representatives, including but not limited to AI developers; and independent domain experts. There are four questions Congress should ask…(More)“.

Paper by Gary King and Steven Worthington: “We show that AI-generated article reviews are now better, by every measure we could construct, than reviews written by well-compensated human experts and judged to be of unusually high quality by journal editors and authors. We then discuss how this result may inform journal policy choices, with concerning downsides as well as potential paths to increase publication speed and quality; reduce adversarial interactions; improve the experience of editors, reviewers, authors, and readers; and advance science…(More)”.

Paper by Fengli Xu et al: “Large language models (LLMs) are transforming computational social science. This Review examines how LLMs now serve four distinct research roles: subjects that simulate human behaviour, assistants that automate analysis, collaborators that generate hypotheses and evaluators that audit and improve research quality. We analyse recent advances across sociology, political science, psychology, anthropology, economics and related fields, revealing how LLMs accelerate discovery while creating new methodological paradigms. This transformation arrives as society itself becomes saturated with artificial intelligence agents—making LLMs both our research tools and authentic research subjects. Yet fundamental challenges persist. LLMs struggle with causal inference and raise urgent ethical concerns about privacy and misinformation, and their ability to reproduce social complexity remains uneven and unproven. We identify critical research directions for harnessing LLMs’ potential while addressing their limitations. As artificial intelligence reshapes human society, understanding this transformation requires the very tools driving it—a recursive challenge that will define the future of social science…(More)”.

Article by Emily Bazelon: “Artificial intelligence is alarming many of its human creators by leaping the guardrails designed to contain it. In the last few months, safety researchers have resigned from OpenAI and Anthropic, warning that their former employers could be courting disaster: A.I. that escapes our control irrevocably. The companies are reportedly gaming out scenarios of a catastrophic cyberattack or other event in the next six to 12 months.

Regulating this powerful and evolving technology would pose serious challenges for any Congress and White House. As it is, President Trump undermined the chances for bipartisan regulation by calling predictions of an A.I. catastrophe a “hoax” in September. Yoshua Bengio, a scientist and godfather of A.I., wrote in October, “I do not believe we are on track to see sufficient government regulation in time.”

What should the government do? We asked a panel of experts to assess the risks A.I. presents and the tools of governance that could be adopted to address them.

Alex Bores is a New York legislator who sponsored the bill on A.I. safety that passed in his state last year. He recently helped found a group, Who Decides, that aims to develop A.I. policies with Democrats.

Kate Brennan, a former Google tech worker, leads the policy agenda on A.I. and corporate power at the AI Now Institute, an independent research institute providing expert analysis on A.I. in the public interest.

Tyler Cowen is a professor of economics at George Mason University and runs the podcast “Conversations With Tyler.” He has worked with Anthropic, OpenAI and Mercor on issues related to A.I. and economics.

Daniel Kokotajlo, a former OpenAI researcher, founded and runs the AI Futures Project, which is best known for publishing AI 2027 (a forecast of the current trajectory of A.I. development) and AI 2040: Plan A (a vision for how it should be regulated instead).

Sneha Revanur is the founder and president of Encode, an advocacy organization that works on passing laws to make A.I. safer. She helped organize a July letter, signed by more than 1,000 employees at A.I. companies, that asked to slow down development…(More)”.

Article by Colin Lecher: “The Arkansas-based data broker Acxiom claims it has data on billions of people around the world, including where they live and how they might spend their money. It says it can even deduce their political leanings, weight, and interest in medical procedures.

But what exactly does the company know?

For Tracey Reed, a nonprofit worker in Oregon, it knew enough to compile a 58-page dossier charting the purported history of her income, employment, and education, not to mention her spending, including everything from her online shopping to charitable giving. Although much of the information was incorrect, she said, the company attempted to log where she lived, complete with latitude and longitude coordinates. And it listed dozens of companies to which it had sold data or inferences about her.

“I think it’s pretty gross that people are treated like sources of wealth to be mined,” she said. “And that’s what these data profiles are, like, ‘Here’s a guide for how to squeeze this person and get money out of them.’”…(More)”.