If you're interested in meeting with this candidate or would share a meal, please add your name/meeting place on her itinerary<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.google.com%2Fdocument%2Fd%2F1sWpDVo8QP4qWh5Yh1Tte_WMKouFNq8uIElk_E22RL6Y%2Fedit%3Fusp%3Dsharing&data=05%7C02%7Cececourtesy-list%40ecn.purdue.edu%7C7f30912c9304435f4a6908dc42d22b91%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638458717636910755%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=cmgo8j388G06joNG52rYWFUEHVASV9%2B7v3u3qyDofRU%3D&reserved=0>. She will be visiting ECE the afternoon of the 19th and the morning of the 20th, hosted by Charlie Hu. ________________________________ Kate Donahue Monday, 3/18/2024, at 10:30 am LWSN 3102AB (In-person only) Title: AI as a resource: strategy, uncertainty, and societal welfare 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. Mary Ann Satterfield Sr. Administrative Assistant Elmore Family School of Electrical and Computer Engineering Max W. and Maileen Brown Hall of Electrical Engineering 465 Northwestern Ave., BHEE 326B West Lafayette, IN 47907 o: 765-494-6389 m: 765-490-6392 f: 765-494-2706 [cid:image001.png@01DA7499.814482A0]<https://www.purdue.edu/?utm_source=signature&utm_medium=email&utm_campaign=purdue>