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.

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. We then propose a neural networkbased 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. Algorithmic decision-making in practice must avoid discrimination and thus be fair to meet legal, ethical, and societal demands (Nkonde, 2019; De-Arteaga et al., 2022; Corbett-Davies et al., 2023). For example, in the U.S., the Fair Housing Act and Equal Credit Opportunity Act stipulate that decisions must not be subject to systematic discrimination by gender, race, or other attributes deemed as sensitive. However, research from different areas has provided repeated evidence that algorithmic decisionmaking is often not fair. A prominent example is Amazon's tool for automatically screening job applicants that was used between 2014 and 2017 (Dastin, 2018). It was later discovered that the underlying algorithm generated decisions that were subject to systematic discrimination against women and thus resulted in a ceteris paribus lower probability of women being hired. Ensuring fairness in off-policy learning is subject to inherent challenges.