[Ececourtesy-list] Mon, Mar 18, 10:30A, LWSN 3102, Purdue Computes-AI/ML Faculty Candidate: Kate Donahue, PhD Candidate/Cornell: AI as a resource: strategy, uncertainty, and societal welfare

[Satterfield, Mary Ann]

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.

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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

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