大厂推荐系统实践中的公平性挑战与应对策略
Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems
- 通过访谈11名从业者，梳理大厂推荐系统公平性工作流程
- 发现定义公平性、平衡多方利益是核心难点
- 适合关注技术落地与跨团队协作的研究者和从业者
推荐系统广泛部署于高影响领域，易受偏见影响并引发大规模社会后果。尽管学术界已提出多种度量与缓解偏见的方法，但将其转化为实际应用仍面临根本性挑战。本研究通过半结构化访谈（N=11），描绘了大型科技公司中推荐系统从业者的实际工作流程，重点关注技术团队如何在内部以及与法务、数据、公平性等团队协作中考虑公平性问题。研究识别出将公平性融入现有推荐系统工作流的关键障碍：在推荐场景中定义公平性的困难、协调多利益相关方的矛盾、应对动态环境变化；同时发现组织层面的挑战，包括为公平性工作分配时间不足，以及跨团队沟通不畅。最后，研究提出可操作建议，面向推荐系统社区中的从业者与人机交互研究者。
原文摘要 · Abstract (English)
Recommender systems (RS), which are widely deployed across high-stakes domains, are susceptible to biases that can cause large-scale societal impacts. Researchers have proposed methods to measure and mitigate such biases - but translating academic theory into practice is inherently challenging. Through a semi-structured interview study (N=11), we map the RS practitioner workflow within large technology companies, focusing on how technical teams consider fairness internally and in collaboration with legal, data, and fairness teams. We identify key challenges to incorporating fairness into existing RS workflows: defining fairness in RS contexts, balancing multi-stakeholder interests, and navigating dynamic environments. We also identify key organization-wide challenges: making time for fairness work and facilitating cross-team communication. Finally, we offer actionable recommendations for the RS community, including practitioners and HCI researchers.
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