Zoom: https://illinois.zoom.us/j/85969325848?pwd=OC9SSVhRaktoNEQrVXc1YmpRdGpRUT09
Abstract:
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.
Bio:
Kate Donahue is a sixth year computer science PhD candidate at Cornell advised by Jon Kleinberg. She works on algorithmic problems relating to the societal impact of AI such as fairness, human/AI collaboration and game-theoretic models of federated learning. Her work has been supported by an NSF fellowship and recognized by a FAccT Best Paper award. During her PhD, she has interned at Amazon, Google, and Microsoft Research.
Faculty Host: Han Zhao, Ruta Mehta
Meeting ID: 859 6932 5848 ; Password: csillinois
