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

arXiv: https://arxiv.org/abs/2303.03367 · HTML: https://mlsanity.com/paper/2303.03367/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2023.

## Built on by (most cited first, 6 of 6)

- [Secondary Stakeholders in AI: Fighting for, Brokering, and Navigating Agency](https://mlsanity.com/paper/2506.07281/) (2025)
- [The AI-DEC: A Card-based Design Method for User-centered AI Explanations](https://mlsanity.com/paper/2405.16711/) (2024)
- [The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation](https://mlsanity.com/paper/2602.11318/) (2026)
- [Positive AI: Key Challenges in Designing Artificial Intelligence for Wellbeing](https://mlsanity.com/paper/2304.12241/) (2023)
- [FairFare: A Tool for Crowdsourcing Rideshare Data to Empower Labor Organizers](https://mlsanity.com/paper/2502.11273/) (2025)
- [FareShare: A Tool for Labor Organizers to Estimate Lost Wages and Contest Arbitrary AI and Algorithmic Deactivations](https://mlsanity.com/paper/2505.08904/) (2025)

## Abstract (arXiv)

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.