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

2025-02-16

AI Summary

Algorithmic opacity in ride-hailing platforms undermines driver income predictability and impedes labor organizations’ evidence-based policy advocacy. Method: This study introduces the first community-driven algorithmic auditing framework designed specifically for labor organizations. Leveraging the FairFare tool, it enables driver crowdsourcing of trip-level data (45 drivers, 76,000+ trips), constructs granular income/expense models, statistically infers platform commission rates, and integrates qualitative interviews with policy impact analysis to bridge technical auditing and legislative advocacy. Contribution/Results: The project directly catalyzed the passage of Colorado Senate Bill SB24-75—a landmark algorithmic transparency law—and advanced national legislative agendas on platform algorithmic accountability. It establishes a replicable, tripartite intervention paradigm—“workers–technology–policy”—for digital labor governance, offering a scalable model for labor-led algorithmic accountability initiatives.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Search and Optimization: Algorithm Configuration
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Security and Privacy: Data transparency and provenance

Abstract

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.

Problem

• Address unpredictable rideshare working conditions
• Increase AI system transparency and accountability
• Empower labor organizers with data tools

Innovation

• Crowdsources rideshare worker data
• Estimates platform take rate
• Influences policy for transparency