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.

翻译：我们提议优化,作为将基于AI的决定模式的公平化正规化的一般范例。我们主张优化模式允许制定广泛的公平标准,作为社会福利功能,同时使AI能够利用高度先进的解决方案技术。我们展示优化模式如何有助于在神经网络、支持矢量机和基于规则的体系方面作出面向公平的决策,在适当的限制下最大限度地发挥社会福利功能。特别是,我们为衡量公平性或公平与效率相结合的各种功能提出可移植的优化模式,其中包括若干不平等指标、罗素标准、麦克洛昂和胡佛指数、阿尔法公平、纳什和卡拉伊-斯莫罗丁斯基谈判解决方案、罗尔西亚和实用主义标准相结合以及统计偏差措施。所有这些模式都可以通过线性方案编制、混合整线/线性方案规划或(在两种情况下)专门的组合式方案编制方法有效解决。