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

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 via 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 periods 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 for automating lost wages estimation for deactivated drivers, through a 6-month partnership with the State of Washington’s largest rideshare labor union. Our 3-month field deployment yielded 178 worker account signups. We observed that the tool could reduce lost wages 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.