FairFare: A Tool for Crowdsourcing Rideshare Data to Empower Labor Organizers
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916828327968768 |
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| author | Calacci, Dana Rao, Varun Nagaraj Dalal, Samantha Di, Catherine Pua, Kok-Wei Schwartz, Andrew Spitzberg, Danny Monroy-Hernández, Andrés |
| author_facet | Calacci, Dana Rao, Varun Nagaraj Dalal, Samantha Di, Catherine Pua, Kok-Wei Schwartz, Andrew Spitzberg, Danny Monroy-Hernández, Andrés |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11273 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FairFare: A Tool for Crowdsourcing Rideshare Data to Empower Labor Organizers Calacci, Dana Rao, Varun Nagaraj Dalal, Samantha Di, Catherine Pua, Kok-Wei Schwartz, Andrew Spitzberg, Danny Monroy-Hernández, Andrés Human-Computer Interaction Artificial Intelligence Computers and Society 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. |
| title | FairFare: A Tool for Crowdsourcing Rideshare Data to Empower Labor Organizers |
| topic | Human-Computer Interaction Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2502.11273 |