Fairness in algorithmic decision-making is often defined in the predictive space, where predictive performance - used as a proxy for decision-maker (DM) utility - is traded off against prediction-based fairness notions, such as demographic parity or equality of opportunity. This perspective, however, ignores how predictions translate into decisions and ultimately into utilities and welfare for both DM and decision subjects (DS), as well as their allocation across social-salient groups. In this paper, we propose a multi-stakeholder framework for fair algorithmic decision-making grounded in welfare economics and distributive justice, explicitly modeling the utilities of both the DM and DS, and defining fairness via a social planner's utility that captures inequalities in DS utilities across groups under different justice-based fairness notions (e.g., Egalitarian, Rawlsian). We formulate fair decision-making as a post-hoc multi-objective optimization problem, characterizing the achievable performance-fairness trade-offs in the two-dimensional utility space of DM utility and the social planner's utility, under different decision policy classes (deterministic vs. stochastic, shared vs. group-specific). Using the proposed framework, we then identify conditions (in terms of the stakeholders' utilities) under which stochastic policies are more optimal than deterministic ones, and empirically demonstrate that simple stochastic policies can yield superior performance-fairness trade-offs by leveraging outcome uncertainty. Overall, we advocate a shift from prediction-centric fairness to a transparent, justice-based, multi-stakeholder approach that supports the collaborative design of decision-making policies.

翻译：算法决策中的公平性通常定义在预测空间内，此时预测性能（作为决策者效用的代理指标）会与基于预测的公平性概念（如人口均等或机会均等）进行权衡。然而，这种视角忽视了预测如何转化为决策，并最终转化为决策者与决策对象的效用和福祉，以及这些效用在各社会显著性群体间的分配。本文提出一个基于福利经济学和分配正义的多利益相关方框架，用于公平算法决策：该框架显式建模决策者与决策对象的效用，通过纳入社会规划者效用（该效用量化不同正义导向的公平概念下各群体间决策对象效用的不平等程度，如平等主义、罗尔斯主义）来定义公平性。我们将公平决策表述为事后多目标优化问题，在不同决策策略类别（确定性vs.随机性、共享式vs.群体特异性）下，刻画决策者效用与社会规划者效用构成的二维效用空间中可实现的性能-公平权衡。利用所提框架，我们识别出随机策略优于确定性策略的条件（以利益相关方效用为表征），并通过实证表明：利用结果不确定性的简单随机策略能产生更优的性能-公平权衡。总体而言，我们倡导从以预测为中心的公平性转向透明、基于正义的多利益相关方方法，以支持决策策略的协作式设计。