Inherent Trade-offs in the Fair Allocation of Treatments
This work addresses the challenge of designing fair treatment policies in real-world settings, such as education, with incremental contributions to algorithmic fairness.
The paper tackles the problem of balancing fairness and overall benefit in algorithmic treatment allocation by proposing a causal framework that learns optimal intervention policies under fairness constraints, demonstrating that affirmative action can dramatically improve overall benefit while preserving fairness, as shown in student test score data.
Explicit and implicit bias clouds human judgement, leading to discriminatory treatment of minority groups. A fundamental goal of algorithmic fairness is to avoid the pitfalls in human judgement by learning policies that improve the overall outcomes while providing fair treatment to protected classes. In this paper, we propose a causal framework that learns optimal intervention policies from data subject to fairness constraints. We define two measures of treatment bias and infer best treatment assignment that minimizes the bias while optimizing overall outcome. We demonstrate that there is a dilemma of balancing fairness and overall benefit; however, allowing preferential treatment to protected classes in certain circumstances (affirmative action) can dramatically improve the overall benefit while also preserving fairness. We apply our framework to data containing student outcomes on standardized tests and show how it can be used to design real-world policies that fairly improve student test scores. Our framework provides a principled way to learn fair treatment policies in real-world settings.