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

Created by
  • Haebom

Author

Dana Calacci, Varun Nagaraj Rao, Samantha Dalal, Catherine Di, Kok-Wei Pua, Andrew Schwartz, Danny Spitzberg, Andrés Monroy-Hern andez.

Outline

This paper addresses the challenges faced by rideshare workers due to the unpredictable work environment caused by the nature of giga-platforms that rely on opaque AI and algorithmic systems. We find that labor organizers need data to support their advocacy efforts for greater transparency and accountability on the platforms. To address this need, we worked with a Colorado-based rideshare union to develop a tool called FairFare. FairFare collects and analyzes worker data to estimate the take rate of the platform. Over 18 months, we deployed FairFare, collecting data on over 76,000 trips from 45 drivers, and in evaluation interviews, labor organizers reported that FairFare influenced the language and passage of Colorado Senate Bill 24-75, which requires transparency and data disclosure in platform operations, and contributed to shaping the national discourse. Finally, we consider the complexities of translating quantitative data into policy outcomes, the nature of community-based audits, and implications for the design of future transparency tools.

Takeaways, Limitations

Takeaways:
Demonstrates how data-driven tools can be effectively leveraged in labor movements to increase transparency and accountability on rideshare platforms.
Proving that data collected through tools like FairFare can drive real policy change (as passed by Colorado Senate Bill 24-75).
Emphasizes the importance of community-based data collection and analysis.
Provided by __T25436_____ on designing future tools for platform transparency.
Limitations:
Further research is needed into the complexity of transforming quantitative data into policy outcomes.
A more in-depth discussion is needed on the limitations and ways to improve community-based auditing.
Further research is needed on the scalability of FairFare and its applicability to other regions/platforms.
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