Double Fairness Policy Learning: Integrating Action Fairness and Outcome Fairness in Decision-making

• URL: http://arxiv.org/abs/2601.19186v1
• Date: Tue, 27 Jan 2026 04:36:19 GMT
• Title: Double Fairness Policy Learning: Integrating Action Fairness and Outcome Fairness in Decision-making
• Authors: Zeyu Bian, Lan Wang, Chengchun Shi, Zhengling Qi,
• Abstract summary: Policy learning induces two distinct fairness targets: action fairness (equitable action assignments) and outcome fairness (equitable downstream consequences)<n>We propose a novel double fairness learning (DFL) framework that explicitly manages the trade-off among three objectives: action fairness, outcome fairness, and trustworthy value.<n>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.
• Score: 25.90320742385333
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Is Softmax Loss All You Need? A Principled Analysis of Softmax-family Loss [91.61796429377041]
  The Softmax loss is one of the most widely employed surrogate objectives for classification and ranking tasks.<n>We investigate whether different surrogates achieve consistency with classification and ranking metrics, and analyze their gradient dynamics to reveal distinct convergence behaviors.<n>Our results establish a principled foundation and offer practical guidance for loss selections in large-class machine learning applications.
  arXiv  Detail & Related papers  (2026-01-30T09:24:52Z)
• Procedural Fairness in Multi-Agent Bandits [6.6764415968019195]
  We introduce a new fairness objective, procedural fairness, which provides equal decision-making power for all agents.<n>We prove that different fairness notions prioritize fundamentally different and incompatible values, highlighting that fairness requires explicit normative choices.
  arXiv  Detail & Related papers  (2026-01-15T17:11:51Z)
• Adversarial Bias: Data Poisoning Attacks on Fairness [48.17618627431355]
  There is relatively little research on how an AI system's fairness can be intentionally compromised.<n>In this work, we provide a theoretical analysis demonstrating that a simple adversarial poisoning strategy is sufficient to induce maximally unfair behavior.<n>Our attack significantly outperforms existing methods in degrading fairness metrics across multiple models and datasets.
  arXiv  Detail & Related papers  (2025-11-11T15:09:53Z)
• A General Incentives-Based Framework for Fairness in Multi-agent Resource Allocation [4.930376365020355]
  We introduce the General Incentives-based Framework for Fairness (GIFF)<n>GIFF is a novel approach for fair multi-agent resource allocation that infers fair decision-making from standard value functions.
  arXiv  Detail & Related papers  (2025-10-30T17:37:51Z)
• Fairness-Aware Reinforcement Learning (FAReL): A Framework for Transparent and Balanced Sequential Decision-Making [41.53741129864172]
  Equity in real-world sequential decision problems can be enforced using fairness-aware methods.<n>We propose a framework where multiple trade-offs can be explored.<n>We show that our framework learns policies that are more fair across multiple scenarios, with only minor loss in performance reward.
  arXiv  Detail & Related papers  (2025-09-26T11:42:14Z)
• A Causal Lens for Learning Long-term Fair Policies [3.2233767737586674]
  This paper highlights the importance of investigating long-term fairness in dynamic decision-making systems.<n>We propose a general framework where long-term fairness is measured by the difference in the average expected qualification gain.<n>We analyze the intrinsic connection between these components and an emerging fairness notion called benefit fairness.
  arXiv  Detail & Related papers  (2025-06-12T19:22:50Z)
• Fairness-Aware Meta-Learning via Nash Bargaining [63.44846095241147]
  We introduce a two-stage meta-learning framework to address issues of group-level fairness in machine learning. The first stage involves the use of a Nash Bargaining Solution (NBS) to resolve hypergradient conflicts and steer the model. We show empirical effects across various fairness objectives in six key fairness datasets and two image classification tasks.
  arXiv  Detail & Related papers  (2024-06-11T07:34:15Z)
• DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision [73.80009454050858]
  This work presents a self-supervised model, called DualFair, that can debias sensitive attributes like gender and race from learned representations. Our model jointly optimize for two fairness criteria - group fairness and counterfactual fairness.
  arXiv  Detail & Related papers  (2023-03-15T07:13:54Z)
• Improving Robust Fairness via Balance Adversarial Training [51.67643171193376]
  Adversarial training (AT) methods are effective against adversarial attacks, yet they introduce severe disparity of accuracy and robustness between different classes. We propose Adversarial Training (BAT) to address the robust fairness problem.
  arXiv  Detail & Related papers  (2022-09-15T14:44:48Z)
• Towards Equal Opportunity Fairness through Adversarial Learning [64.45845091719002]
  Adversarial training is a common approach for bias mitigation in natural language processing. We propose an augmented discriminator for adversarial training, which takes the target class as input to create richer features.
  arXiv  Detail & Related papers  (2022-03-12T02:22:58Z)
• Off-Policy Imitation Learning from Observations [78.30794935265425]
  Learning from Observations (LfO) is a practical reinforcement learning scenario from which many applications can benefit. We propose a sample-efficient LfO approach that enables off-policy optimization in a principled manner. Our approach is comparable with state-of-the-art locomotion in terms of both sample-efficiency and performance.
  arXiv  Detail & Related papers  (2021-02-25T21:33:47Z)
• Inherent Trade-offs in the Fair Allocation of Treatments [2.6143568807090696]
  Explicit and implicit bias clouds human judgement, leading to discriminatory treatment of minority groups. We propose a causal framework that learns optimal intervention policies from data subject to fairness constraints.
  arXiv  Detail & Related papers  (2020-10-30T17:55:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.