# FairFare: A Tool for Crowdsourcing Rideshare Data to Empower Labor Organizers

arXiv: https://arxiv.org/abs/2502.11273 · HTML: https://mlsanity.com/paper/2502.11273/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 107 references that are in the map.
- First posted on arXiv 16 Feb 2025.

## Builds on (most cited first, 16 of 16)

- [Power to the People? Opportunities and Challenges for Participatory AI](https://mlsanity.com/paper/2209.07572/) (2022)
- [Black-Box Access is Insufficient for Rigorous AI Audits](https://mlsanity.com/paper/2401.14446/) (2024)
- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors](https://mlsanity.com/paper/2105.02980/) (2021)
- [Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling](https://mlsanity.com/paper/2402.17861/) (2024)
- [Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies](https://mlsanity.com/paper/2012.09995/) (2020)
- [Sociotechnical Audits: Broadening the Algorithm Auditing Lens to Investigate Targeted Advertising](https://mlsanity.com/paper/2308.15768/) (2023)
- [The Future of HCI-Policy Collaboration](https://mlsanity.com/paper/2409.19738/) (2024)
- [Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes](https://mlsanity.com/paper/2303.03367/) (2023)
- [Rideshare Transparency: Translating Gig Worker Insights on AI Platform Design to Policy](https://mlsanity.com/paper/2406.10768/) (2024)
- [Navigating Multi-Stakeholder Incentives and Preferences: Co-Designing Alternatives for the Future of Gig Worker Well-Being](https://mlsanity.com/paper/2302.13436/) (2023)
- [Understanding Human Intervention in the Platform Economy: A case study of an indie food delivery service](https://mlsanity.com/paper/2303.03523/) (2023)
- [Discrimination through Image Selection by Job Advertisers on Facebook](https://mlsanity.com/paper/2306.07527/) (2023)
- [Data Collectives as a means to Improve Accountability, Combat Surveillance and Reduce Inequalities](https://mlsanity.com/paper/2409.00737/) (2024)
- [Gig2Gether: Data-sharing to Empower, Unify and Demystify Gig Work](https://mlsanity.com/paper/2502.04482/) (2025)
- ["At the end of the day, I am accountable": Gig Workers' Self-Tracking for Multi-Dimensional Accountability Management](https://mlsanity.com/paper/2403.19436/) (2024)

## Built on by (most cited first, 1 of 1)

- [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)

Rideshare workers experience unpredictable working conditions due to gig work platforms' reliance on opaque AI and algorithmic systems. In response to these challenges, we found that labor organizers want data to help them advocate for legislation to increase the transparency and accountability of these platforms. To address this need, we collaborated with a Colorado-based rideshare union to develop FairFare, a tool that crowdsources and analyzes workers' data to estimate the take rate -- the percentage of the rider price retained by the rideshare platform. We deployed FairFare with our partner organization that collaborated with us in collecting data on 76,000+ trips from 45 drivers over 18 months. During evaluation interviews, organizers reported that FairFare helped influence the bill language and passage of Colorado Senate Bill 24-75, calling for greater transparency and data disclosure of platform operations, and create a national narrative. Finally, we reflect on complexities of translating quantitative data into policy outcomes, nature of community based audits, and design implications for future transparency tools.