In an attempt to make algorithms fair, the machine learning literature has largely focused on equalizing decisions, outcomes, or error rates across race or gender groups. To illustrate, consider a hypothetical government rideshare program that provides transportation assistance to low-income people with upcoming court dates. Following this literature, one might allocate rides to those with the highest estimated treatment effect per dollar, while constraining spending to be equal across race groups. That approach, however, ignores the downstream consequences of such constraints, and, as a result, can induce unexpected harms. For instance, if one demographic group lives farther from court, enforcing equal spending would necessarily mean fewer total rides provided, and potentially more people penalized for missing court. Here we present an alternative framework for designing equitable algorithms that foregrounds the consequences of decisions. In our approach, one first elicits stakeholder preferences over the space of possible decisions and the resulting outcomes--such as preferences for balancing spending parity against court appearance rates. We then optimize over the space of decision policies, making trade-offs in a way that maximizes the elicited utility. To do so, we develop an algorithm for efficiently learning these optimal policies from data for a large family of expressive utility functions. In particular, we use a contextual bandit algorithm to explore the space of policies while solving a convex optimization problem at each step to estimate the best policy based on the available information. This consequentialist paradigm facilitates a more holistic approach to equitable decision-making.

翻译：为了使算法公平，机器学习文献主要关注于在种族或性别群体间均衡决策、结果或错误率。以一项假设性的政府拼车项目为例，该项目为有出庭日期的低收入人群提供交通援助。按照现有文献的思路，我们可能会将拼车服务分配给每单位成本下处理效果估计值最高的人群，同时强制要求不同种族群体的支出相等。然而，这种方法忽视了此类约束的下游后果，从而可能引发意想不到的危害。例如，若某个族群居住地距离法院较远，强制执行支出均衡必然导致提供的拼车总次数减少，进而可能使更多人因错过出庭而受到惩罚。本文提出了一种替代框架，用于设计强调决策后果的公平算法。在我们的方法中，首先需收集利益相关者对可能决策空间及相应结果的偏好——例如在支出均衡与出庭率之间的权衡偏好。随后，我们通过优化决策策略空间，以最大化所获效用的方式做出权衡。为此，我们开发了一种算法，能够从数据中高效学习大规模表达性效用函数下的最优策略。具体而言，我们采用上下文赌博机算法探索策略空间，同时在每一步求解凸优化问题，以基于现有信息估算最佳策略。这种后果主义范式为公平决策提供了更全面的方法论路径。