Fairness has become a central concern in ranking problems involving individuals or social groups, particularly under the Responsible Artificial Intelligence agenda. In Multi-Criteria Decision Analysis, Stochastic Multicriteria Acceptability Analysis (SMAA) provides a robust framework for handling uncertainty and incomplete preference information, but it does not explicitly address fairness in the resulting rankings. This paper proposes SMAA-Fair, a fairness-aware extension of SMAA for ranking problems. The approach reweights the simulated rankings generated by SMAA according to their level of group fairness, so that fairer rankings contribute more strongly to the acceptability indices and central weights vector. The framework is independent of the aggregation model and can incorporate different fairness metrics. In this study, Statistical Parity, normalized discounted Kullback--Leibler divergence (rKL) and normalized discounted cumulative Kullback--Leibler divergence (nDKL) are adopted. Rankings are derived from the fairness-adjusted acceptability matrix using expected ranking and maximum acceptability ranking. We also derive the central weight according to the degree of fairness in the obtained rankings. Numerical experiments with synthetic and real data show that SMAA-Fair improves the representation of protected groups among favourable ranking positions, while preserving robustness to preference uncertainty.

翻译：公平性已成为涉及个人或社会群体的排序问题中的核心关切，特别是在负责任人工智能议程框架下。在多准则决策分析中，随机多准则可接受性分析（SMAA）为处理不确定性和不完全偏好信息提供了稳健框架，但未明确解决由此产生的排序结果中的公平性问题。本文提出SMAA-Fair，一种面向公平性的SMAA排序扩展方法。该方法根据模拟排序的群体公平性水平重新加权SMAA生成的排序结果，使更公平的排序对可接受性指标和中心权重向量产生更强贡献。该框架独立于聚合模型，并可纳入不同公平性度量。本研究采用统计均等性、归一化折扣Kullback-Leibler散度（rKL）和归一化折扣累积Kullback-Leibler散度（nDKL）。通过期望排序和最大可接受性排序从经公平性调整的可接受性矩阵中导出最终排序。我们还根据所得排序的公平性程度推导中心权重。基于合成数据与真实数据的数值实验表明，SMAA-Fair在保持对偏好不确定性鲁棒性的同时，提升了受保护群体在有利排序位置中的代表性。