In this session, Raso explores how technologies that administer border and immigration policies construct another equally important, but less explored, subject: state agency. Drawing on a recent example from Canada, Raso demonstrates how digitalization simultaneously concentrates state power while diffusing agency, reflecting on what this means for legal accountability mechanisms and decision-making.

In this talk, Suresh Venkatasubramanian will dissect the Blueprint for an AI Bill of Rights, discuss what its authors were trying to achieve, illustrate the difficult issues of technology and policy that had to be resolved, and expand on where this effort is going.

In this talk, Ariel D. Stern will explore how the rapid innovations of digital technology are changing definitions and approaches towards healthcare by practitioners, institutions, and users, and why these changes require new and innovative approaches towards policy and regulation.

Our weekly SRI Seminar Series welcomes Kobbi Nissim, the McDevitt Chair of Computer Science at Georgetown University, and an affiliate professor at Georgetown Law. Nissim’s research works towards establishing rigorous practices for privacy in computation. He is particularly interested in intersection points between privacy and various disciplines within and outside computer science, including cryptography, machine learning, game theory, complexity theory, algorithmics, statistics, databases, and more recently privacy law and policy.

In this session, David G. Rand explores the role of political polarization in the sharing and belief of misinformation, suggesting that it is possible to supplement forms of professional fact-checking by harnessing the wisdom of crowds. Presenting data from survey experiments, Rand will demonstrate that successful crowdsourcing of misinformation identification may succeed because of, rather than in spite of, polarization and political motivations.

In this session, Sven Nyholm will discuss “responsibility gaps” and asymmetries regarding praise and blame for outcomes produced by artificial intelligence (AI) technologies. Using contemporary examples such as text produced by large language models, accidents caused by self-driving cars, and medical diagnoses and treatment, Nyholm will demonstrate how praise for good outcomes produced by AI is typically harder to deserve than blame for bad outcomes.