FairFare: A Tool for Crowdsourcing Rideshare Data to Empower Labor Organizers
This work addresses the need for data to empower labor organizers advocating for rideshare workers, though it is incremental as it builds on existing transparency tools.
The researchers tackled the problem of opaque AI systems in rideshare gig work by developing FairFare, a tool that crowdsourced data from 45 drivers over 76,000+ trips to estimate platform take rates, which helped influence the passage of Colorado Senate Bill 24-75 for greater transparency.
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.