Join us for a thought-provoking conversation with Ana-Andreea Stoica on Designing for Society: AI in Networks, Markets, and Platforms.
Abstract from the speaker:
AI systems increasingly reshape our networks, markets, and platforms. When deployed in social environments—online platforms, labor markets, and information ecosystems—AI interacts with complex human behavior, strategic incentives, and structural inequality. This talk focuses on foundational challenges and opportunities for AI systems: how to design and evaluate algorithmic interventions in complex social environments. I will present recent work on causal inference under competing treatments, which formalizes how competition for user attention and strategic behavior among experimenters distort experimental data and invalidate naïve estimates of algorithmic impact. By modeling experimentation as a strategic data acquisition problem, we show how evaluation itself becomes an optimization problem, and we derive mechanisms that recover meaningful estimates despite interference and competition. I connect this problem to deriving foundational properties of systems that enable reliable experimentation. Beyond this case study, the talk highlights broader implications for the design and evaluation of AI systems in networks, markets, and platforms. I argue that responsible deployment requires rethinking evaluation methodologies to account for incentives, feedback loops, and system-level effects, and I outline directions for how algorithmic and statistical tools can tackle these challenges.
About the speaker:
Ana-Andreea Stoica is a Research Group Leader in the Social Foundations of Computation Department at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. Her work centers algorithms and evaluation methods for multi-objective optimization problems with applications in society. Ana’s research combines efficiency and societal objectives using unsupervised learning techniques, game theory, and behavioral and sociological modeling. From algorithmic design with fairness considerations to evaluating resource-allocation systems with equity constraints and incentive alignment, Ana is particularly interested in designing algorithms to improve people’s access to information and opportunities. Ana holds a Ph.D. in Computer Science from Columbia University and a B.A. in Mathematics from Princeton University. Since 2019, Ana has been co-organizing the EAAMO Bridges initiative. Ana co-founded the ACM conference series on Equity and Access in Algorithms, Mechanisms, and Optimization and served as a Program Co-Chair for the inaugural edition.
Bring your own lunch bag! Light pastries and drinks will be available in case you forget to bring it.
The Data Science Brown Bag Series is an informal and interactive gathering where participants bring their own brown bag lunch and engage in discussions on research and insights the field of data and computational social science (light pastries and drinks will be available if you forget your lunch bag!). The series provides a platform for data enthusiasts, researchers, and practitioners to share their experiences, best practices, and emerging methodologies and research in using data science to analyze and understand social and political phenomena. The brown bag talk series is for anyone interested in data science and social science to network, learn, and share ideas in a casual and friendly setting.
