曝光偏差被传统公平性指标忽略，新方法能发现并缓解这一问题。
Fairness in Link Prediction Beyond Demographic Parity: A Reproducibility Study
- 提出基于排名的NDKL度量，捕捉链接位置差异带来的不公平。
- 实验表明Δ_DP在掩盖偏差的同时，新指标可有效识别暴露不公。
- 推荐给关注推荐系统公平性的研究者与工程师。
在公平排序链接预测中，群体均等性（Δ_DP）是常用公平性度量。然而，Mattos等人（2025）指出，该指标因忽略链接在排序中的位置而无法检测曝光偏差。本研究通过复现证实：即使某些群体间链接系统性地排在更低位置，Δ_DP仍可能显示总体均等。相比之下，提出的秩感知归一化折扣KL散度（NDKL）能有效检测此类差异。我们还复现了后处理方法MORAL的有效性——其在保持良好性能的同时显著提升基于曝光的公平性。进一步在合成同质性设置、类别敏感属性及多种公平性与效用指标（包括子群对适配的注意力加权排序公平性，AWRF）下评估鲁棒性。结果表明，基于曝光的度量能揭示Δ_DP所隐藏的偏见，且MORAL在不同数据集和设置下均以极小的效用损失减轻这些偏见。代码已公开于 https://github.com/Floris93100/reproducing-MORAL。
原文摘要 · Abstract (English)
In fair ranked link prediction, demographic parity ($Δ_\mathrm{DP}$) is a common fairness metric. Yet, Mattos et al. (2025) argue that it fails to detect exposure bias because it ignores where links appear in the ranking. In this study, we reproduce this claim by showing that $Δ_\mathrm{DP}$ can indicate aggregate parity even when some subgroup-pair links are systematically ranked lower than others. The proposed rank-aware Normalized Discounted KL-divergence (NDKL), however, does detect such disparities. We also reproduce the effectiveness of MORAL, a post-processing method that improves exposure-based fairness while maintaining competitive utility. Beyond reproduction, we assess robustness using synthetic homophily settings, categorical sensitive attributes, and additional fairness and utility metrics, including subgroup-pair-adapted Attention-Weighted Rank Fairness (AWRF). Overall, our results show that exposure-based metrics uncover biases hidden by $Δ_\mathrm{DP}$ and that MORAL reduces these biases with minimal utility loss across diverse settings and datasets. We release a corrected, reproducible implementation at https://github.com/Floris93100/reproducing-MORAL.
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