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Fairness through Social Welfare Optimization
We propose social welfare optimization as a general paradigm for formalizing fairness in AI systems.
Online Convex Optimization Perspective for Learning from Dynamically Revealed Preferences
We study the problem of online learning (OL) from revealed preferences: a learner wishes to learn a non-strategic agent's private utility function through observing the agent's utility-maximizing actions in a changing environment.
Balancing Fairness and Efficiency in an Optimization Model
Optimization models generally aim for efficiency by maximizing total benefit or minimizing cost.
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