发表机构
Université Paris-Saclay; University of Regina（巴黎萨克雷大学; 里贾纳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对经典聚合规则忽视公平、多样与包容的问题，提出基于图摘要的AURORA方法，在聚合中嵌入EDI约束，在五个数据集上实现更优的公平-多样性权衡。
AI 中文摘要
将不同用户群体的偏好聚合成集体结果，会引发公平性、多样性与包容性（EDI）方面的根本性挑战：诸如 Borda 和 Condorcet 等经典聚合规则缺乏防止结果系统性偏向多数群体、坍缩为同质化条目或对少数群体代表性不足的机制。我们通过受 EDI 约束的图摘要来解决这一问题。用户偏好被建模为加权属性二分图，一种贪心粗化算法迭代合并用户节点，同时强制执行三个结构性 EDI 标准：公平差距约束（$\Delta E$）、列表内多样性约束（ILD）和群体包容性约束。我们的方法不是在聚合后修正公平性，而是将 EDI 保持直接嵌入图结构。我们在涵盖四个领域的五个数据集上进行了评估：MovieLens 100k 和 1M、this http URL、Rate My Professors 以及 OpenAlex（2018-2023）。我们的方法 AURORA 相对于经典投票规则取得了最大且最一致的多样性提升，并且在 MovieLens 100k 上，当 $k = 20$ 时，它同时改进了相对于 Borda 和 Condorcet 的所有三个 EDI 标准。在 Rate My Professors 上，它以适度的公平成本，结合了高多样性（ILD = 0.808）和最高的女性条目代表性（60%），并在 OpenAlex 上实现了最低的公平差距（$\Delta E = 0.031$），而基于 Borda 的方法推荐了零位女性作者。在 this http URL 上，这是唯一一个敏感属性出现在二分图两侧的数据集，我们的方法并未降低公平差距，我们将这一局限性与此前的研究发现联系起来，即人口统计均等并不总是一个合适的目标。这些结果表明，将 EDI 约束嵌入聚合结构比事后方法能产生更稳健的公平-多样性权衡。
英文摘要
Aggregating the preferences of diverse user groups into a collective outcome raises fundamental challenges of equity, diversity, and inclusion (EDI): classical aggregation rules such as Borda and Condorcet have no mechanism to prevent results from systematically favoring majority groups, collapsing onto homogeneous items, or under-representing minorities. We address this problem through EDI-constrained graph summarization. User preferences are modeled as a weighted attributed bipartite graph, and a greedy coarsening algorithm iteratively merges user nodes while enforcing three structural EDI criteria: an equity gap constraint ($ΔE$), an intra-list diversity constraint (ILD), and a group inclusion constraint. Rather than correcting fairness after aggregation, our method embeds EDI preservation directly into the graph structure. We evaluate across five datasets spanning four domains: MovieLens 100k and 1M, libimseti.cz, Rate My Professors, and OpenAlex (2018-2023). Our method, AURORA, achieves the largest and most consistent diversity gains over classical voting rules, and on MovieLens 100k at $k = 20$ it simultaneously improves all three EDI criteria over both Borda and Condorcet. On Rate My Professors, it combines high diversity (ILD = 0.808) with the highest female item representation (60%), at a moderate equity cost, and it achieves the lowest equity gap ($ΔE = 0.031$) on OpenAlex, where Borda-based methods recommend zero female authors. On libimseti.cz, the only dataset where the sensitive attribute is present on both sides of the bipartite graph, our method does not reduce the equity gap, a limitation we connect to prior findings that demographic parity is not always an appropriate target. These results demonstrate that embedding EDI constraints into aggregation structure yields more robust fairness-diversity trade-offs than post-hoc approaches.
Comments22 pages, 3 figures. Interactive dashboard: https://sitaacisse-boop.github.io/EDI/
