Schwartz Reisman Institute (SRI) AI Bootcamp

The Schwartz Reisman Institute hosts a day-long AI Bootcamp, focusing on research at the frontier of AI and the study of institutions.

About this event
Machine learning and artificial intelligence are rapidly transforming how economies and societies function. Institutions and organizations play a key role in organizing and regulating this transformation, which AI researchers are just beginning to explore.

In collaboration with the Society for Institutional and Organizational Economics (SIOE), the Schwartz Reisman Institute for Technology and Society is offering an opportunity for social scientists who study institutions and organizations to gain an introduction to this emerging and promising field of study with an SRI AI Bootcamp at SIOE 2022. Speakers include leading researchers from MIT, DeepMind, and the University of California, Berkeley.

The SRI AI Bootcamp will begin on Thursday, June 23rd with a day of tutorials taught by leading AI researchers who are engaging with institutions and organizations. SIOE will then host a plenary panel on Saturday, June 25th with paper presentations from this emerging field. Participation in the SRI AI Bootcamp Tutorials is open and free to all, but space is limited and advance registration is mandatory. Attendance at the plenary session is included with registration for the SIOE conference.

Location:

Flavelle House, 78 Queen’s Park Crescent West, Toronto, ON, M5S 2C5

*Please note this venue observes mandatory masking indoors.

Featured speakers:

Irene Chen, PhD Candidate, Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology.

Marzyeh Ghassemi, Assistant Professor, Electrical Engineering and Computer Science (EECS) and the Institute for Medical Engineering & Science (IMES), Massachusetts Institute of Technology.

Gillian Hadfield, Director of the Schwartz Reisman Institute for Technology and Society; Professor, Faculty of Law and Rotman School of Management, University of Toronto.

Dylan Hadfield-Menell, Assistant Professor, Faculty of Artificial Intelligence and Decision Making, Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology.

Joel Leibo, Research Scientist, DeepMind.

Jonathan Stray, Senior Scientist, Berkeley Center for Human-Compatible AI, University of California Berkeley.

Schedule:

Opening Tutorial | June 23, 2022 | 9:00 AM – 12:00 PM ET

In our morning tutorial, we’ll introduce participants to the basic methods used by modern machine learning researchers (supervised, self-supervised and unsupervised learning and reinforcement learning); the framework used to simulate and analyze multi-agent systems composed of artificial agents; the formal tests used by machine learning researchers to evaluate algorithmic bias and develop explanations; and the techniques used to construct the recommender systems that shape social media, online commerce, and more.

Afternoon Tutorial | June 23, 2022 | 2:00 PM – 4:00 PM ET

In our afternoon tutorial, we’ll discuss research in machine learning that invokes institutions and organizations that are well known to the SIOE audience. We’ll discuss the insights available for AI from incomplete contracting theory, how contracts might be deployed in multiagent settings, what we learn about the tragedy of the commons, norms and culture from multiagent simulations, how algorithmic bias and explainability challenges might impact organizational design and regulation, and how democratic processes might be devised to improve the alignment of algorithms and recommender systems with social welfare.

Plenary Session | June 25, 2022 | 8:45 AM – 10:15 AM ET

Gillian Hadfield, “Judging facts, judging norms: Training machine learning models to judge humans requires a new approach to labeling data”

Joel Leibo, “Deep reinforcement learning models the emergent dynamics of human cooperation”

Marzyeh Ghassemi, “Just following AI orders: When unbiased people are influenced by biased AI”

About the speakers:

Irene Chen is a PhD candidate in the Clinical Machine Learning group at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL), advised by David Sontag. Chen’s research centers on machine learning methods for improving clinical care and making it more equitable, as well as auditing and addressing bias in algorithmic models. Her work has been published in machine learning conferences (NeurIPS, AAAI) and medical journals (Nature Medicine, Lancet Digital Health), and has been covered by media outlets including MIT Tech Review, NPR/WGBH, and Stat News. Chen has been named a Rising Star in EECS by University of California Berkeley, Harvard, and University of Maryland. Prior to her PhD, Chen received her AB/SM from Harvard and worked at Dropbox.

Marzyeh Ghassemi is an assistant professor at MIT in Electrical Engineering and Computer Science (EECS) and the Institute for Medical Engineering & Science (IMES), a faculty member at the Vector Institute, a faculty affiliate at the Schwartz Reisman Institute for Technology and Society, and holds a Canadian CIFAR AI Chair and Canada Research Chair. She holds MIT affiliations with the Jameel Clinic and CSAIL. Ghassemi holds a Herman L. F. von Helmholtz Career Development Professorship and was named a CIFAR Azrieli Global Scholar and one of MIT Tech Review’s 35 Innovators Under 35. Previously, she was a Visiting Researcher with Alphabet’s Verily and an assistant professor at the University of Toronto. Prior to her PhD in Computer Science at MIT, she received an MSc in biomedical engineering from Oxford University as a Marshall Scholar, and BS degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University. Ghassemi has previously served as a NeurIPS Workshop Co-Chair and General Chair for the ACM Conference on Health, Inference and Learning (CHIL). She also founded the non-profit Association for Health Learning and Inference. Ghassemi has published across computer science and clinical venues, including NeurIPS, KDD, AAAI, MLHC, JAMIA, JMIR, JMLR, AMIA-CRI, Nature Medicine, Nature Translational Psychiatry, and Critical Care. Her work has been featured in popular press such as MIT News, NVIDIA, and The Huffington Post.

Gillian Hadfield is a professor of law and professor of strategic management at the University of Toronto and holds the Schwartz Reisman Chair in Technology and Society. She is the inaugural Director of the Schwartz Reisman Institute for Technology and Society. Her current research is focused on innovative design for legal and regulatory systems for AI and other complex global technologies; computational models of human normative systems; and working with machine learning researchers to build ML systems that understand and respond to human norms. Hadfield is a faculty member at the Vector Institute for Artificial Intelligence in Toronto and at the Center for Human-Compatible AI at the University of California Berkeley and Senior Policy Advisor at OpenAI in San Francisco. Her book Rules for a Flat World: Why Humans Invented Law and How to Reinvent It for a Complex Global Economy was published by Oxford University Press in 2017; a paperback edition with a new prologue on AI was published in 2020 and an audiobook version released in 2021.

Dylan Hadfield-Menell is an assistant professor on the faculty of Artificial Intelligence and Decision-Making in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT). Hadfield-Menell’s research focuses on the problem of agent alignment: the challenge of identifying behaviors that are consistent with the goals of another actor or group of actors. He runs the Algorithmic Alignment Group, which works to identify algorithmic solutions to alignment problems that arise from groups of AI systems, principal-agent pairs (e.g., human-robot teams), and societal oversight of machine learning systems.

Joel Leibo is a research scientist at DeepMind and a research affiliate with the McGovern Institute for Brain Research at MIT. His research explores how deep reinforcement learning agents can be trained to perform complex cognitive behaviors like cooperating in groups, evaluating the performance of deep reinforcement learning agents, and how model processes like cumulative culture gave rise to unique aspects of human intelligence.

Jonathan Stray is a senior scientist at the Berkeley Center for Human-Compatible AI working on recommender systems—algorithms that select and rank content across social media, news apps, streaming music and video, and online shopping. For a decade, Stray taught the double masters in computer science and journalism at Columbia Journalism School. He led development of Workbench, a visual programming system, and Overview, an open-source document analysis system for journalists. He was formerly an editor at the Associated Press, a writer for the New York Times, Foreign Policy, ProPublica, MIT Technology Review, and Wired, and a computer graphics designer at Adobe.

About the Schwartz Reisman Institute for Technology and Society:

The Schwartz Reisman Institute for Technology and Society (SRI) was established through a generous gift from Canadian entrepreneurs Gerald Schwartz and Heather Reisman in 2019. SRI is a research and solutions hub within the University of Toronto dedicated to ensuring that powerful technologies like artificial intelligence are safe, fair, ethical, and make the world better—for everyone. SRI devel­ops new modes of thinking in order to better understand the social implications of technologies in the present age, and works to reinvent laws, institutions, and social values to ensure that technol­ogy is designed, governed, and deployed to deliver a more just and inclusive world. SRI researchers range in fields from law to computer science, engineering, philosophy, political science, and beyond. SRI draws on world-class expertise across univer­sities, government, industry, and community organizations to unite fundamental research on emerging technologies with actionable solutions for public policy, law, the private sector, and citizens alike.