FareShare: A Tool for Labor Organizers to Estimate Lost Wages and Contest Arbitrary AI and Algorithmic Deactivations

• URL: http://arxiv.org/abs/2505.08904v1
• Date: Tue, 13 May 2025 18:46:47 GMT
• Title: FareShare: A Tool for Labor Organizers to Estimate Lost Wages and Contest Arbitrary AI and Algorithmic Deactivations
• Authors: Varun Nagaraj Rao, Samantha Dalal, Andrew Schwartz, Amna Liaqat, Dana Calacci, Andrés Monroy-Hernández,
• Abstract summary: Deactivation-the abrupt removal of gig workers' platform access-typically occurs through arbitrary AI and algorithmic decisions with little explanation or recourse.<n>This represents one of the most severe forms of algorithmic control and often devastates workers' financial stability.<n>Recent U.S. state policies now mandate appeals processes and recovering compensation during the period of wrongful deactivation.<n>We designed FareShare, a computational tool automating lost wage estimation for deactivated drivers.
• Score: 9.632548567569636
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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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