Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes
March 06, 2023 Β· Declared Dead Β· π International Conference on Human Factors in Computing Systems
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Authors
Angie Zhang, Alexander Boltz, Jonathan Lynn, Chun-Wei Wang, Min Kyung Lee
arXiv ID
2303.03367
Category
cs.HC: Human-Computer Interaction
Citations
45
Venue
International Conference on Human Factors in Computing Systems
Last Checked
3 months ago
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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