Ke Li, Research Scientist
Google
In this talk, I will present our work on overcoming two long-standing problems in machine learning and computer vision:
Abel Molina, PhD candidate
David R. Cheriton School of Computer Science
Mohammad Hajiabadi, Postdoctoral Researcher
Computer Science Division, EECS Department
Renjie Liao, Department of Computer Science
University of Toronto
Kate Larson
David R. Cheriton School of Computer Science
Axiomatic approaches are an appealing method for designing fair algorithms, as they provide a formal structure for reasoning about and rationalizing individual decisions. However, to make these algorithms useful in practice, their axioms must appropriately capture social norms.
We explore this tension between fairness axioms and socially acceptable decisions in the context of cooperative game theory for the fair division of rewards.
Masoumeh Shafieinejad, PhD candidate
David R. Cheriton School of Computer Science
Carl Yang, Department of Computer Science
University of Illinois at Urbana–Champaign
Niao He, Department of Industrial Systems Engineering and Coordinated Science Laboratory
University of Illinois at Urbana-Champaign
Ryan Clancy, Master’s candidate
Vikas Garg, Electrical Engineering & Computer Science
Massachusetts Institute of Technology