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arXiv · 2303.03367

Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes

Angie Zhang·Alexander Boltz·Jonathan Lynn·Chun-Wei Wang·Min Kyung Lee
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Abstract

AI technologies continue to advance from digital assistants to assisted decision-making. However, designing AI remains a challenge given its unknown outcomes and uses. One way to expand AI design is by centering stakeholders in the design process. We conduct co-design sessions with gig workers to explore the design of gig worker-centered tools as informed by their driving patterns, decisions, and personal contexts. Using workers' own data as well as city-level data, we create probes -- interactive data visuals -- that participants explore to surface the well-being and positionalities that shape their work strategies. We describe participant insights and corresponding AI design considerations surfaced from data probes about: 1) workers' well-being trade-offs and positionality constraints, 2) factors that impact well-being beyond those in the data probes, and 3) instances of unfair algorithmic management. We discuss the implications for designing data probes and using them to elevate worker-centered AI design as well as for worker advocacy.

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Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, Min Kyung Lee. 2023-03-06. Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes. https://doi.org/10.1145/3544548.3581354

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