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FairFare: A Tool for Crowdsourcing Rideshare Data to Empower Labor Organizers
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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.
Forward citations
Cited by 4 Pith papers
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A DSAR-based longitudinal audit of 1.5 million UK Uber trips finds that after dynamic pricing, pay per hour fell, standby time rose, pay predictability dropped, and Uber's median take rate increased.
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A Node on the Constellation: The Role of Feminist Makerspaces in Building and Sustaining Alternative Cultures of Technology Production
Feminist makerspaces endure by resisting growth and institutional funding, instead relying on care-driven stewardship, solidarity with local justice groups, and shared governance.
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Aggregated Individual Reporting for Post-Deployment Evaluation
The authors formalize a mechanism for collecting and aggregating public reports about deployed AI systems, aiming to surface unknown harms and enable accountability.
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FareShare: A Tool for Labor Organizers to Estimate Lost Wages and Contest Arbitrary AI and Algorithmic Deactivations
FareShare automates lost wage estimation for deactivated rideshare drivers, reducing reported calculation time by over 95% during a three-month deployment, though the evaluation is mostly qualitative and small-scale.
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