An emerging body of research indicates that ineffective cross-functional collaboration -- the interdisciplinary work done by industry practitioners across roles -- represents a major barrier to addressing issues of fairness in AI design and development. In this research, we sought to better understand practitioners' current practices and tactics to enact cross-functional collaboration for AI fairness, in order to identify opportunities to support more effective collaboration. We conducted a series of interviews and design workshops with 23 industry practitioners spanning various roles from 17 companies. We found that practitioners engaged in bridging work to overcome frictions in understanding, contextualization, and evaluation around AI fairness across roles. In addition, in organizational contexts with a lack of resources and incentives for fairness work, practitioners often piggybacked on existing requirements (e.g., for privacy assessments) and AI development norms (e.g., the use of quantitative evaluation metrics), although they worry that these tactics may be fundamentally compromised. Finally, we draw attention to the invisible labor that practitioners take on as part of this bridging and piggybacking work to enact interdisciplinary collaboration for fairness. We close by discussing opportunities for both FAccT researchers and AI practitioners to better support cross-functional collaboration for fairness in the design and development of AI systems.

翻译：新兴研究表明，低效的跨职能协作——即行业从业者跨角色开展的跨学科工作——是解决AI设计与开发中公平性问题的主要障碍。本研究旨在深入理解从业者当前实施跨职能协作以实现AI公平性的实践与策略，从而识别支持更有效协作的机遇。我们对来自17家公司的23位不同岗位的行业从业者进行了系列访谈和设计研讨会。研究发现，从业者通过开展"桥接工作"以克服跨角色间围绕AI公平性存在的理解、情境化与评估摩擦。此外，在缺乏公平性工作资源与激励的组织环境中，从业者往往借助现有需求（如隐私评估）和AI开发规范（如使用量化评估指标）推进公平性工作，但他们担忧这些策略可能存在根本性缺陷。最后，我们揭示了从业者在此类桥接与借力工作中承担的隐形劳动——这些劳动构成了实现公平性跨学科协作的基础。本文结尾探讨了FAccT研究者与AI从业者如何更好地支持AI系统设计与开发中面向公平性的跨职能协作。