Abstract

In this talk, I’ll describe some recent work outlining how distribution shifts are fundamental to working with human-centric data. Some of these shifts come from attempting to “join” datasets gathered in different contexts,  others may be the result of people’s preferences affecting which data they provide to which systems, and even more can arise when peoples’ preferences themselves are shaped by ML systems’ recommendations. Each of these types of shift require different modeling and analysis to more accurately predict the behavior of ML pipelines deployed in a way where they interact repeatedly with people who care about their predictions.

Short Bio

Jamie is an assistant professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. She was previously an assistant professor in the School of Computer Science at Georgia Tech. Prior to starting as faculty, she was hosted by Michael Kearns, Aaron Roth, and Rakesh Vohra as a Warren Center fellow at the University of Pennsylvania. She completed her PhD working with Avrim Blum at Carnegie Mellon University. Her work studies the social impact of machine learning and the impact of social behavior on ML’s guarantees. How should machine learning be made robust to behavior of the people generating training or test data for it? How should ensure that the models we design do not exacerbate inequalities already present in society?

Short Bio

Paola Ricaurte is an associate professor in the Department of Media and Digital Culture at Tecnológico de Monterrey and faculty associate at the Berkman Klein Center for Internet & Society at Harvard University. With Nick Couldry and Ulises Mejías, she co-founded Tierra Común, a network of academics, practitioners and activists interested in decolonizing data. She participates in several expert committees, such as the Global Partnership for Artificial Intelligence (GPAI), the Global Index on Responsible AI and the Expert Group for the Implementation of the UNESCO Recommendation on the Ethics of Artificial Intelligence. She is a member of the Alliance for Inclusive Algorithms and leads the Latin American and Caribbean hub of the Feminist AI Research Network, fr. In addition to her academic work, she participates in civil society initiatives to promote digital rights and the development of public interest technologies.

Short Bio

Kate Larson is a professor and University Research Chair in the Cheriton School of Computer Science, University of Waterloo. She is interested in algorithmic questions arising in artificial intelligence and multiagent systems with a particular focus on algorithmic game theory, group decision making, preference modelling, and the insights that reinforcement learning can bring to these problems, along with ways of promoting and supporting cooperative AI. She is co-editor-in-chief of the Journal of Autonomous Agents and Multiagent System and will serve as program chair for IJCAI 2024. She is a former president of the International Foundation of Autonomous Agents and Multiagent Systems, was the General Chair for the 2017 International Conference on Autonomous Agents and Multiagent Systems (AAMAS) and served as an associate editor for both the Journal of Artificial Intelligence Research (JAIR) and Artificial Intelligence (AIJ). She regularly serves as Area Chair for several AI conferences due to her expertise in multiagent systems.

Short Bio

Gary Marchant, Ph.D., J.D., M.P.P., is Regents’ Professor and Faculty Director of the Center for Law, Science & Innovation at the Sandra Day O’Connor College of Law, Arizona State University (ASU). Professor Marchant’s research interests include the governance of emerging technologies such as genomics, biotechnology, nanotechnology, artificial intelligence, neuroscience and blockchain. Prior to joining ASU in 1999, Professor Marchant was a partner at the Washington, D.C., office of Kirkland & Ellis. He has authored more than 200 articles, books and book chapters on various issues relating to emerging technologies. He has served on six National Academies of Science, Engineering and Medicine (NASEM) consensus committees, is an elected lifetime member of the American Law Institute and is a Fellow of the American Association for the Advancement of Science. He also chairs the IEEE Working Group (P2863) to create a governance standard for entities that develop or use artificial intelligence.

Short Bio

Dr. Roxana Daneshjou received her undergraduate degree at Rice University in Bioengineering. She completed her MD/PhD at Stanford, where she worked in the lab of Dr. Russ Altman studying pharmacogenomics in diverse populations. During this time, she was a Howard Hughes Medical Institute Medical Scholar and a Paul and Daisy Soros Fellowship for New Americans Fellow.  She completed dermatology residency at Stanford in the research track and completed a postdoc with Dr. James Zou in Biomedical Data Science working on artificial intelligence for healthcare. She is an incoming assistant professor of Biomedical Data Science and Dermatology.

Short Bio

Atoosa Kasirzadeh is a philosopher, mathematician, and systems engineer. She is an assistant professor in the philosophy department and the Director of Research at the Centre for Technomoral Futures at the University of Edinburgh, and a Research Lead at the Alan Turing Institute. Prior to this, she was at Google DeepMind in London and a postdoctoral research fellow at the Australian National University. Her research is focused on the short- and long-term social implications of Artificial intelligence, with a particular focus on value alignment, generative AI, and recommender systems.