Fairness is a central pillar of trustworthy machine learning, especially in domains where accuracy- or profit-driven optimization is insufficient. While most fairness research focuses on supervised learning, fairness in policy learning remains less explored. Because policy learning is interventional, it induces two distinct fairness targets: action fairness (equitable action assignments) and outcome fairness (equitable downstream consequences). Crucially, equalizing actions does not generally equalize outcomes when groups face different constraints or respond differently to the same action. We propose a novel double fairness learning (DFL) framework that explicitly manages the trade-off among three objectives: action fairness, outcome fairness, and value maximization. We integrate fairness directly into a multi-objective optimization problem for policy learning and employ a lexicographic weighted Tchebyshev method that recovers Pareto solutions beyond convex settings, with theoretical guarantees on the regret bounds. Our framework is flexible and accommodates various commonly used fairness notions. Extensive simulations demonstrate improved performance relative to competing methods. In applications to a motor third-party liability insurance dataset and an entrepreneurship training dataset, DFL substantially improves both action and outcome fairness while incurring only a modest reduction in overall value.

翻译：公平性是可信机器学习的重要支柱，尤其在仅靠精度或利润驱动的优化不足的领域。尽管大多数公平性研究集中于监督学习，策略学习中的公平性仍较少被探索。由于策略学习具有干预性，它引出了两个不同的公平性目标：行动公平性（公平的行动分配）和结果公平性（公平的下游后果）。关键的是，当不同群体面临不同约束或对相同行动反应不同时，平等化行动通常不会平等化结果。我们提出了一种新颖的双重公平性学习（DFL）框架，该框架明确管理三个目标之间的权衡：行动公平性、结果公平性和价值最大化。我们将公平性直接整合到策略学习的多目标优化问题中，并采用字典序加权切比雪夫方法，该方法能在非凸设置下恢复帕累托解，并在遗憾界上具有理论保证。我们的框架灵活，可容纳各种常用的公平性概念。大量仿真实验表明，相对于竞争方法，其性能有所提升。在应用于机动车第三方责任保险数据集和创业培训数据集时，DFL显著提高了行动和结果的公平性，同时仅导致整体价值的适度降低。