开发工具收集网约车数据，助力劳工组织推动平台透明化立法
FairFare: A Tool for Crowdsourcing Rideshare Data to Empower Labor Organizers
- 通过众包方式收集司机真实行程数据，估算平台抽成比例
- 累计采集45名司机18个月7.6万+次行程数据，支撑政策倡导
- 直接影响科罗拉多州法案制定，推动全国性平台透明化讨论
网约车司机因平台依赖不透明的算法系统而面临工作条件不稳定问题。为应对这一挑战，我们发现劳工组织亟需数据以推动立法，提升平台透明度与问责性。为此，我们与科罗拉多州一家网约车工会合作开发了FairFare工具，用于众包收集并分析司机数据，估算平台抽成率（take rate）。该工具在合作组织支持下部署，共收集45名司机、76,000+次行程数据，历时18个月。评估访谈显示，FairFare帮助影响了科罗拉多州参议院第24-75号法案的条文设计与通过，该法案要求平台公开运营数据、提升透明度，并在全球范围内形成倡导范例。最后，本文反思了量化数据向政策转化的复杂性、社区审计的特征，以及未来透明度工具的设计启示。
原文摘要 · Abstract (English)
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.
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