Algorithmic decision-making in practice must be fair for legal, ethical, and societal reasons. To achieve this, prior research has contributed various approaches that ensure fairness in machine learning predictions, while comparatively little effort has focused on fairness in decision-making, specifically off-policy learning. In this paper, we propose a novel framework for fair off-policy learning: we learn decision rules from observational data under different notions of fairness, where we explicitly assume that observational data were collected under a different potentially discriminatory behavioral policy. For this, we first formalize different fairness notions for off-policy learning. We then propose a neural network-based framework to learn optimal policies under different fairness notions. We further provide theoretical guarantees in the form of generalization bounds for the finite-sample version of our framework. We demonstrate the effectiveness of our framework through extensive numerical experiments using both simulated and real-world data. Altogether, our work enables algorithmic decision-making in a wide array of practical applications where fairness must be ensured.

翻译：算法决策在实践中必须出于法律、伦理和社会原因确保公平性。为实现这一目标，已有研究提出了多种确保机器学习预测公平性的方法，然而针对决策公平性（特别是离线策略学习）的研究相对较少。本文提出了一种新型公平离线策略学习框架：我们从观测数据中学习符合不同公平性定义的决策规则，并明确假设这些观测数据是在一个可能存在歧视行为的异构策略下收集的。为此，我们首先形式化定义了离线策略学习中的多种公平性概念，随后提出基于神经网络的框架，用于学习满足不同公平性约束的最优策略。我们进一步为框架的有限样本版本提供了泛化界形式的理论保证。通过使用模拟数据和真实数据开展的大量数值实验，验证了所提框架的有效性。综上，本研究使得在必须确保公平性的多种实际应用场景中实现算法决策成为可能。