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.

Register

Featured speakers:

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

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.

Location:

This event will take place at the University of Toronto’s St. George Campus. Exact location details will be announced to registrants before the event.

Questions?

Contact us at sri.events@utoronto.ca.

Schedule:

Opening Tutorial | June 23, 2022 | 9:00 AM – 11:00 AM 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 | 9:00 AM – 10:30 AM ET

SIOE attendees have exclusive access to Saturday’s plenary panel which will include paper presentations from Dylan Hadfield-Menell, Joel Leibo, and Marzyeh Ghassemi.