AI governance, once a speculative thought experiment, is now a high-stakes arena marked by policy experiments, institutional improvisation, and the colliding rhetoric of hype and harm. We find ourselves in a governance moment where large language models destabilize assumptions faster than institutions can adapt, and where the demand for structural critique must coexist with the need for operational pathways.
At the 2025 ACM Conference on Fairness, Accountability, and Transparency (FAccT) this June, CNTR Director Suresh Venkatasubramanian gave one of the keynote talks, addressing what meaningful governance looks like in a world remade by generative AI and what it means to succeed — or fail — in shaping it.
Suresh’s keynote talk will be available on the FAccT YouTube Channel.
“Are we winning yet? FAccT, AI governance, and the shape of what comes next,”
Abstract:
We created FAccT as a space for rigorous critique, deep interdisciplinarity, and structural honesty about the role of technology in society. In the years since, the field — and the world around it — has changed in ways both thrilling and troubling. AI governance, once a speculative thought experiment, is now a high-stakes arena marked by policy experiments, institutional improvisation, and the colliding rhetoric of hype and harm. And the work at FAccT and other venues constitutes a living breathing paradigm for analyzing, critiquing, and governing sociotechnical systems.
That paradigm now faces new pressures. We find ourselves in a governance moment where large language models destabilize assumptions faster than institutions can adapt, and where the demand for structural critique must coexist with the need for operational pathways. What does meaningful governance look like in a world remade by generative AI? What does it mean to succeed — or fail — in shaping it? And how do we, as a community, continue to build something that is both honest and useful?
About the ACM Conference on Fairness, Accountability, and Transparency (FAccT):
ACM FAccT is an interdisciplinary conference dedicated to bringing together a diverse community of scholars from computer science, law, social sciences, and humanities to investigate and tackle issues in this emerging area. Research challenges are not limited to technological solutions regarding potential bias, but include the question of whether decisions should be outsourced to data- and code-driven computing systems. We particularly seek to evaluate technical solutions with respect to existing problems, reflecting upon their benefits and risks; to address pivotal questions about economic incentive structures, perverse implications, distribution of power, and redistribution of welfare; and to ground research on fairness, accountability, and transparency in existing legal requirements.