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
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.
• Human-centered computing → Human computer interaction (HCI).
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Install the CLIlune papers fulltext e5a444f4-bd83-4f4c-89b8-31513101d6a5Cited by top-tier papers19
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