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.

翻译：推荐系统（RS）被广泛应用于高风险领域，但容易受到偏见影响，从而可能引发大规模的社会影响。研究者已提出多种方法来衡量和缓解此类偏见——然而将学术理论转化为实践本身具有挑战性。通过一项半结构化访谈研究（N=11），我们描绘了大型科技公司内推荐系统从业者的工作流程，重点关注技术团队如何在内部以及与法律、数据和公平性团队协作时考量公平性。我们识别出将公平性纳入现有推荐系统工作流程的关键挑战：在推荐系统情境中定义公平性、平衡多方利益相关者的利益，以及应对动态环境。同时，我们也指出了组织层面的关键挑战：为公平性工作安排时间以及促进跨团队沟通。最后，我们为推荐系统社区（包括从业者和人机交互研究者）提供了可行的建议。