Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice

• URL: http://arxiv.org/abs/2306.06542v1
• Date: Sat, 10 Jun 2023 23:42:26 GMT
• Title: Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice
• Authors: Wesley Hanwen Deng, Nur Yildirim, Monica Chang, Motahhare Eslami, Ken Holstein, Michael Madaio
• Abstract summary: An emerging body of research indicates that ineffective cross-functional collaboration represents a major barrier to addressing issues of fairness in AI design and development. 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.
• Score: 10.979734542685447
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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