Suresh Venkatasubramanian

Computer science professor and former White House adviser Suresh Venkatasubramanian criticizes the Senate's AI policy roadmap for favoring tech industry, lacking regulations on bias and deception. Discussion highlights the roadmap's shortcomings, absence of legislative proposals, and the need for concrete solutions addressing societal implications and regulatory oversight.

In episode 55 of The Gradient Podcast, Daniel Bashir speaks to Professor Suresh Venkatasubramanian. Professor Venkatasubramanian is a Professor of Computer Science and Data Science at Brown University, where his research focuses on algorithmic fairness and the impact of automated decision-making systems in society. He recently served as Assistant Director for Science and Justice in the White House Office of Science and Technology Policy, where he co-authored the Blueprint for an AI Bill of Rights.Have suggestions for future podcast guests (or other feedback)? Let us know here!Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (02:25) Suresh’s journey into AI and policymaking* (08:00) The complex graph of designing and deploying “fair” AI systems* (09:50) The Algorithmic Lens* (14:55) “Getting people into a room” isn’t enough* (16:30) Failures of incorporation* (21:10) Trans-disciplinary vs interdisciplinary, the limiting nature of “my lane” / “your lane” thinking, going beyond existing scientific and philosophical ideas* (24:50) The trolley problem is annoying, its usefulness and limitations* (25:30) Breaking the frame of a discussion, self-driving doesn’t fit into the parameters of the trolley problem* (28:00) Acknowledging frames and their limitations* (29:30) Social science’s inclination to critique, flaws and benefits of solutionism* (30:30) Computer security as a model for thinking about algorithmic protections, the risk of failure in policy* (33:20) Suresh’s work on recourse* (38:00) Kantian autonomy and the value of recourse, non-Western takes and issues with individual benefit/harm as the most morally salient question* (41:00) Community as a valuable entity and its implications for algorithmic governance, surveillance systems* (43:50) How Suresh got involved in policymaking / the OSTP* (46:50) Gathering insights for the AI Bill of Rights Blueprint* (51:00) One thing the Bill did miss… Struggles with balancing specificity and vagueness in the Bill* (54:20) Should “automated system” be defined in legislation? Suresh’s approach and issues with the EU AI Act* (57:45) The danger of definitions, overlap with chess world controversies* (59:10) Constructive vagueness in law, partially theorized agreements* (1:02:15) Digital privacy and privacy fundamentalism, focus on breach of individual autonomy as the only harm vector* (1:07:40) GDPR traps, the “legacy problem” with large companies and post-hoc regulation* (1:09:30) Considerations for legislating explainability* (1:12:10) Criticisms of the Blueprint and Suresh’s responses* (1:25:55) The global picture, AI legislation outside the US, legislation as experiment* (1:32:00) Tensions in entering policy as an academic and technologist* (1:35:00) Technologists need to learn additional skills to impact policy* (1:38:15) Suresh’s advice for technologists interested in public policy* (1:41:20) OutroLinks:* Suresh is on Mastodon @geomblog@mastodon.social (and also Twitter)* Suresh’s blog* Blueprint for an AI Bill of Rights* Papers* Fairness and abstraction in sociotechnical systems* A comparative study of fairness-enhancing interventions in machine learning* The Philosophical Basis of Algorithmic Recourse* Runaway Feedback Loops in Predictive Policing Get full access to The Gradient at thegradientpub.substack.com/subscribe

Suresh Venkatasubramanian, a data science professor at Brown University, discusses the urgent need for accountability in AI. He joins fellow researchers to defend their important letter advocating against bias and discrimination in artificial intelligence. They emphasize the growing recognition of these issues and the political challenges ahead, particularly in light of recent U.S. executive actions. Venkatasubramanian highlights the collective responsibility of academics in promoting fairness and transparency in AI systems to foster better governance.