AI as a resource: strategy, uncertainty, and societal welfare

In recent years, humanity has been faced with a new resource - artificial intelligence. AI can be a boon to society, or can also have negative impacts, especially with inappropriate use. My research agenda studies the societal impact of AI, particularly focusing on AI as a resource and on the strategic decisions that agents make in deciding how to use it.

In this talk, I will consider some of the key strategic questions that arise in this framework: the decisions that agents make in jointly constructing and sharing AI models, and the decisions that they make in dividing tasks between their own expertise and the expertise of a model. The first of these questions has motivated my work on "model-sharing games", which models scenarios such as federated learning or data cooperatives. In this setting, we view agents with data as game-theoretic players and analyze questions of stability, optimality, and fairness. Secondly, I will describe some of my work in modeling human-algorithm collaboration. In particular, I will describe work on best-item recovery in categorical prediction, showing how differential accuracy rates and anchoring on algorithmic suggestions can influence overall performance and describe some ongoing work inspired by human-LLM interaction.