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

2025-05-13

AI Summary

Ride-hailing platforms’ algorithmic, unannounced driver deactivation leads to quantifiable wage loss that is difficult for drivers to document and contest. Method: This study designs and implements an automated wage-loss estimation tool for labor rights advocacy. It introduces the first computational framework balancing interpretability, regulatory compliance, and low technical barrier—directly embedding jurisdiction-specific policies (e.g., Washington State’s deactivation compensation rules) into the workflow. The system employs historical income–weighted time-series modeling, automated CSV/Excel parsing, PDF report generation, and privacy-preserving local deployment. Contribution/Results: The tool improves维权 efficiency significantly: manual calculation time is reduced by over 95%, and data-entry errors are eliminated. Within three months, it supported 178 labor organizers across multiple jurisdictions and contributed to several successful arbitration appeals. It establishes a reusable techno-institutional paradigm for algorithmic labor governance—integrating technical design with policy implementation to advance algorithmic accountability and worker protection.

Technology Category

• Search and Optimization: Algorithm Configuration
• Humans and AI: Crowd Sourcing and Human Computation
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• Estimating lost wages for gig workers after AI-driven deactivation
• Automating complex workflows for labor organizers and legal teams
• Addressing trust and adoption challenges in high-stakes labor contexts

Innovation

• Automates lost wage estimation for deactivated drivers
• Reduces calculation time by over 95%
• Generates arbitration-ready reports efficiently