Achieving Cooperative Outcomes in Mixed Settings
Explore a thought-provoking lecture from the 2024 Cooperative AI Summer School on achieving cooperative outcomes in mixed settings. Delve into the insights of Nisarg Shah, an Associate Professor of computer science at the University of Toronto and recognized innovator in AI. Discover how Shah's research at the intersection of computer science and economics addresses fairness, efficiency, elicitation, and incentives in algorithmic decision-making. Learn about theoretical foundations for fairness in voting, resource allocation, and machine learning. Gain insights from Shah's work on not-for-profit websites that have helped over 200,000 users make fair and optimal decisions. Benefit from the expertise of an award-winning researcher who has been recognized as part of "Innovators Under 35" and "AI's 10 to Watch." This 58-minute lecture offers valuable knowledge for those interested in cooperative AI and its applications in mixed settings.