提出系统级公平性框架，解决多模型推荐系统中的不公平问题
From Models to Systems: A Comprehensive Fairness Framework for Compositional Recommender Systems
- 从系统视角建模推荐流程中各组件的公平性交互
- 实验证明仅关注单模型公平性无法解决系统级偏差
- 用贝叶斯优化联合优化用户满意度与公平性
机器学习公平性研究通常聚焦于单个模型的公平表现，但现实推荐系统由多个模型和阶段构成，包括候选召回、打分与服务等环节，这给负责任的研发与部署带来挑战。正如欧盟人工智能法案所强调的，需超越将模型视为独立实体的审计方式。本文提出一个系统级公平性建模框架，关注不同用户群体所获得的最终效用，并考虑召回与打分模型等组件间的相互作用。我们形式化揭示了仅关注模型级公平性的局限性，强调需要能处理用户偏好异质性的新工具。为缓解系统级不平等，我们采用封闭盒优化工具（如贝叶斯优化）联合优化效用与公平性。在合成数据与真实数据集上的实证表明，该框架有效提升了系统整体公平性，凸显了构建系统级框架的必要性。
原文摘要 · Abstract (English)
Fairness research in machine learning often centers on ensuring equitable performance of individual models. However, real-world recommendation systems are built on multiple models and even multiple stages, from candidate retrieval to scoring and serving, which raises challenges for responsible development and deployment. This system-level view, as highlighted by regulations like the EU AI Act, necessitates moving beyond auditing individual models as independent entities. We propose a holistic framework for modeling system-level fairness, focusing on the end-utility delivered to diverse user groups, and consider interactions between components such as retrieval and scoring models. We provide formal insights on the limitations of focusing solely on model-level fairness and highlight the need for alternative tools that account for heterogeneity in user preferences. To mitigate system-level disparities, we adapt closed-box optimization tools (e.g., BayesOpt) to jointly optimize utility and equity. We empirically demonstrate the effectiveness of our proposed framework on synthetic and real datasets, underscoring the need for a system-level framework.
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