发表机构
School of Business Stevens Institute of Technology（史蒂文斯理工学院商学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出端到端公平性优化框架及公平决策聚焦学习范式，结合多任务学习处理预测与决策阶段的公平性问题，通过数值实验验证其在资源分配场景的有效性。
AI 中文摘要
许多现实系统依赖预测模型辅助决策，公平性问题同时出现在预测和决策阶段。我们提出端到端公平性优化（E2EFO）作为统一框架，将公平性整合到从预测到决策的全流程中。我们聚焦基于群体公平性的资源分配问题：预测任务估算分配影响，同时限制各群体间的精度差异；决策任务通过优化基于群体的α-公平性指标，公平分配这些影响。在该框架内，我们提出公平决策聚焦学习（FDFL），这一训练范式同时考虑预测精度、预测公平性和决策遗憾——即因预测不完美导致的决策公平性损失。FDFL通过梯度下降训练预测器，利用多任务学习技术结合目标梯度。核心计算挑战是关于预测器参数的决策雅可比矩阵：我们针对一类可处理的公平分配推导了精确闭式公式，并在一般情况下应用可微优化层。我们进一步为标量化的FDFL目标建立了有限样本泛化界。在基于医疗场景的单资源分配和合成多资源分配上的数值实验表明，在预测辅助决策中同时考虑预测公平性和决策公平性具有重要价值。
英文摘要
Many real-world systems rely on predictive models to inform decisions, and fairness concerns arise in both the prediction and decision stages. We introduce end-to-end fairness optimization (E2EFO) as a unifying framework that integrates fairness across the prediction-to-decision pipeline. We focus on resource allocation with group-based fairness: the prediction task estimates allocation impacts while limiting accuracy disparity across groups, and the decision task distributes those impacts equitably by optimizing a group-based alpha-fairness measure. Within this framework, we propose fair decision-focused learning (FDFL), a training paradigm that jointly accounts for prediction accuracy, prediction fairness, and decision regret -- the loss in decision fairness due to imperfect predictions. FDFL trains the predictor by gradient descent, combining the objective gradients through multi-task learning techniques. The core computational challenge is the decision Jacobian with respect to the predictor parameters: we derive exact closed-form formulas for a tractable class of fair allocation and apply a differentiable optimization layer in the general case. We further establish a finite-sample generalization bound for the scalarized FDFL objective. Numerical experiments on a healthcare-based single resource allocation and a synthetic multiple resource allocation illustrate the value of jointly accounting for prediction fairness and decision fairness in prediction-informed decision-making.
