帮网约车司机算被封号损失的工资，让维权更高效
FareShare: A Tool for Labor Organizers to Estimate Lost Wages and Contest Arbitrary AI and Algorithmic Deactivations
- 用自动化工具计算封号期间的收入损失
- 可减少95%以上计算时间并消除人工错误
- 适合工会组织者和面临算法封号的劳动者使用
当网约车司机突然被平台封号，失去接单和收入渠道时，这种由人工智能和算法做出的任意解雇行为往往缺乏解释与申诉途径，严重威胁其经济稳定。美国部分州已要求建立申诉机制并补偿错误封号期间的收入。然而，劳动组织者仍缺乏有效工具来支持复杂且易出错的维权流程。我们与华盛顿州最大网约车工会合作6个月，开发了FareShare工具，用于自动化估算被封号司机的收入损失。在后续3个月的实地部署中，该工具共注册178个账号。实际应用显示，它将收入计算时间减少95%以上，彻底消除手动输入错误，并帮助法律团队更高效生成仲裁报告。此外，部署过程也揭示了高风险劳动场景中信任、知情同意与工具采纳等社会技术挑战。
原文摘要 · Abstract (English)
What happens when a rideshare driver is suddenly locked out of the platform connecting them to riders, wages, and daily work? Deactivation-the abrupt removal of gig workers' platform access-typically occurs through arbitrary AI and algorithmic decisions with little explanation or recourse. This represents one of the most severe forms of algorithmic control and often devastates workers' financial stability. Recent U.S. state policies now mandate appeals processes and recovering compensation during the period of wrongful deactivation based on past earnings. Yet, labor organizers still lack effective tools to support these complex, error-prone workflows. We designed FareShare, a computational tool automating lost wage estimation for deactivated drivers, through a 6 month partnership with the State of Washington's largest rideshare labor union. Over the following 3 months, our field deployment of FareShare registered 178 account signups. We observed that the tool could reduce lost wage calculation time by over 95%, eliminate manual data entry errors, and enable legal teams to generate arbitration-ready reports more efficiently. Beyond these gains, the deployment also surfaced important socio-technical challenges around trust, consent, and tool adoption in high-stakes labor contexts.
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