Many fairness criteria constrain the policy or choice of predictors. In this work, we propose a different framework for thinking about fairness: Instead of constraining the policy or choice of predictors, we consider which utility a policy is optimizing for. We define value of information fairness and propose to not use utilities that do not satisfy this criterion. We describe how to modify a utility to satisfy this fairness criterion and discuss the consequences this might have on the corresponding optimal policies.

翻译：许多公平性标准对预测器的策略或选择施加约束。在本文中，我们提出了一种关于公平性的不同思考框架：我们不限制预测器的策略或选择，而是考虑政策所优化的效用。我们定义了信息价值公平性，并提出不应使用不满足该标准的效用。我们描述了如何修改效用以满足该公平性标准，并讨论了这可能会对相应最优策略产生的影响。