Investigating Practices and Opportunities for Cross-functional
  Collaboration around AI Fairness in Industry Practice
Wesley Hanwen Deng Nur Yildirim Monica Chang Motahhare Eslami Ken Holstein Michael Madaio
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.