Kate Donahue, Cornell University: “Fairness, efficiency, and uncertainty in societal resource allocation models”

Position: PhD Candidate

Current Institution: Cornell University

Abstract: Fairness, efficiency, and uncertainty in societal resource allocation models

The key question my research studies is “how can we use societal resources to do the most good” ? These “resources” could be concrete like doctors or abstract like data and the objective of “good” could be concepts like fairness privacy or utilization (helping as many people as possible). For example some of my past work has analyzed cases with limited resource allocation studying the extent to which fairness may be achievable alongside other goals such as utilization or stability of certain arrangements. In other papers I model data as a resource analyzing federated learning through the lens of cooperative game theory to explore the relationship between stability optimality and fairness of federating structures. Finally in ongoing work I view human time as a resource modeling how human and AI collaboration can show when complementary performance may be achievable.”


Kate Donahue is a fourth 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 distributed learning. Her PhD has included internships at Microsoft Research and Amazon and has been supported by an NSF fellowship. Previously she was a data scientist working at Booz Allen Hamilton as well as a researcher in evolutionary game theory at the Program of Evolutionary Dynamics at Harvard. Her undergraduate degree was at Harvard a major in math with a minor in statistics.