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

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 crossfunctional collaboration for fairness in the design and development of AI systems.

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“…HCI research highlights the difficulty of eliciting input from domain stakeholders in AI design and development, especially in early ideation and problem formulation phases to inform what is the right thing to design [22,39,69,104]. Prior work noted that stakeholders with little to no background in data science or AI (e.g., domain experts, UX designers, policymakers, etc) might be involved in the design of an AI system's user interface, but rarely in conversations around the objective of the underlying model or the overall problem formulation [39,41,109,133,144]. Recently, a growing body of work in HCI and AI has called for human-centered approaches for broadening participation in AI design to meaningfully engage domain stakeholders to brainstorm and reflect on whether an envisioned future technology is in fact addressing the right problem in the first place [10,34,35,40,70,78,151].…”

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“…Pemanfaatan big data dalam analisis pajak telah mendapatkan perhatian yang signifikan dari para peneliti dan otoritas pajak Iskandar & Kaltum, 2022b, 2022aJaman & Pertiwi, 2023). Salah satu bidang eksplorasi yang menonjol adalah kepatuhan dan penghindaran pajak (Deng et al, 2023;Neuman & Sheu, 2022;Thakuriah et al, 2017). Para peneliti telah menggunakan kumpulan data yang luas untuk memodelkan dan memprediksi perilaku wajib pajak, yang berkontribusi pada pemahaman yang lebih baik tentang faktor-faktor yang mempengaruhi kepatuhan pajak (Ahrens & Bothner, 2020;Deng et al, 2023;Mundy & Thornthwaite, 2011;Priyanka & Singh, n.d.).…”

“…Salah satu bidang eksplorasi yang menonjol adalah kepatuhan dan penghindaran pajak (Deng et al, 2023;Neuman & Sheu, 2022;Thakuriah et al, 2017). Para peneliti telah menggunakan kumpulan data yang luas untuk memodelkan dan memprediksi perilaku wajib pajak, yang berkontribusi pada pemahaman yang lebih baik tentang faktor-faktor yang mempengaruhi kepatuhan pajak (Ahrens & Bothner, 2020;Deng et al, 2023;Mundy & Thornthwaite, 2011;Priyanka & Singh, n.d.). Selain itu, penggunaan analisis data besar telah memungkinkan otoritas pajak untuk meningkatkan kemampuan audit mereka dan mengidentifikasi potensi penghindar pajak secara lebih efektif (Deng et al, 2023;Neuman & Sheu, 2022;Thakuriah et al, 2017).…”

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