Fairness through Optimization

• URL: http://arxiv.org/abs/2102.00311v2
• Date: Tue, 2 Feb 2021 01:55:39 GMT
• Title: Fairness through Optimization
• Authors: Violet Xinying Chen, J.N. Hooker
• Abstract summary: We argue that optimization models allow formulation of a wide range of fairness criteria as social welfare functions. We show how optimization models can assist fairness-oriented decision making in the context of neural networks, support vector machines, and rule-based systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose optimization as a general paradigm for formalizing fairness in AI-based decision models. We argue that optimization models allow formulation of a wide range of fairness criteria as social welfare functions, while enabling AI to take advantage of highly advanced solution technology. We show how optimization models can assist fairness-oriented decision making in the context of neural networks, support vector machines, and rule-based systems by maximizing a social welfare function subject to appropriate constraints. In particular, we state tractable optimization models for a variety of functions that measure fairness or a combination of fairness and efficiency. These include several inequality metrics, Rawlsian criteria, the McLoone and Hoover indices, alpha fairness, the Nash and Kalai-Smorodinsky bargaining solutions, combinations of Rawlsian and utilitarian criteria, and statistical bias measures. All of these models can be efficiently solved by linear programming, mixed integer/linear programming, or (in two cases) specialized convex programming methods.

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