NSF Award
2312774
Nearly every aspect of modern life involves predictions of machine learning (ML) models: e.g., where we choose to shop, live, or apply for jobs. ML models are built and maintained by service providers, who use predictions to offer services to individuals. Based on prediction quality, individuals choose amongst services, or may choose to use none. This interaction gives rise to an ML-enabled market wherein the decisions made by providers and individuals are highly consequential. As individuals choose amongst services, such services gain or lose not only customers, but also access to data that allows them to refine their predictions. The dynamics of interactions between providers and users with varied motivations are more complex than traditional market models. This project aims to develop the theoretical and algorithmic foundations for characterizing and shaping ML-enabled market conditions and algorithmic tools for achieving improved social outcomes. Beyond the theoretical contributions, products of this project will support the development of policy for governing ML-enabled markets in a fair and equitable manner. The project's educational goals include mentoring researchers at the undergraduate and graduate levels, developing new course materials, and promoting dialog on equity in ML through talks, reading groups, and topical workshop activities coordinated with the NSF Institute for Foundations of Data Science at University of Washington.<br/><br/>Interactions between individuals, represented by data, and providers, represented by prediction functions in ML-enabled markets, give rise to complex dynamics, and competitive or cooperative games. The choices of both providers and individuals may be strategic or myopic, depending on whether agents anticipate how their choices will affect future market conditions. Providers and users may act according to a variety of objectives: predictive accuracy (service quality), market share, privacy, fairness, or even adversarial intent. This project analyzes the complex interactions between providers and individuals in ML-enabled markets, with a research agenda comprised of three thrusts: (1) Characterize the participation game and dynamics that arise when users with a variety of objectives act strategically, while providers use data myopically to improve the accuracy of their predictions; (2) Characterize the prediction--retention game and dynamics that arise between strategic providers (including the incentives and social costs of strategic behavior), when users choose their participation level myopically; (3) Combine insights from the first two threads to design algorithmic interventions that improve metrics of outcomes such as social welfare and fairness in ML-enabled markets. Carrying out this agenda will entail developing new theory and algorithms at the intersection of game theory, statistical learning, dynamical systems, and optimization. Challenges due to nonlinear dynamics, nonconvex landscapes, and information limitations will be addressed, and the equilibrium landscape of competitive games with novel structure will be characterized, contributing to the core fields of game theory and machine learning, and their social impact.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
