Not Even Nice Work If You Can Get It; A Longitudinal Study of Uber's Algorithmic Pay and Pricing

2025-06-18

AI Summary

This study investigates how dynamic pricing algorithms employed by platforms such as Uber affect drivers’ livelihoods. Drawing on a longitudinal dataset of 1.5 million trips completed by 258 drivers in the UK, the research integrates participatory action research with a novel, worker-led algorithmic audit framework. Results demonstrate that dynamic pricing systematically reduces drivers’ average earnings, increases platform commission rates, diminishes predictability in job allocation and remuneration, exacerbates income inequality among drivers, and lengthens unpaid idle waiting time. Beyond empirically exposing mechanisms of algorithmic labor exploitation, the study advances theoretical development in “worker data science” and introduces methodological innovations in algorithmic auditing. It further proposes evidence-based intervention pathways and an interdisciplinary analytical framework for platform labor governance.

Technology Category

• Search and Optimization: Algorithm Configuration
• Philosophy and Ethics of AI: AI & Jobs/Labor
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Ride-sharing platforms like Uber market themselves as enabling `flexibility' for their workforce, meaning that drivers are expected to anticipate when and where the algorithm will allocate them jobs, and how well remunerated those jobs will be. In this work we describe our process of participatory action research with drivers and trade union organisers, culminating in a participatory audit of Uber's algorithmic pay and work allocation, before and after the introduction of dynamic pricing. Through longitudinal analysis of 1.5 million trips from 258 drivers in the UK, we find that after dynamic pricing, pay has decreased, Uber's cut has increased, job allocation and pay is less predictable, inequality between drivers is increased, and drivers spend more time waiting for jobs. In addition to these findings, we provide methodological and theoretical contributions to algorithm auditing, gig work, and the emerging practice of worker data science.

Problem

• Analyzing Uber's algorithmic pay and pricing impact on drivers
• Investigating decreased pay and increased inequality post dynamic pricing
• Contributing to algorithm auditing and gig work research methodologies

Innovation

• Participatory audit of Uber's algorithmic pay
• Longitudinal analysis of 1.5 million trips
• Methodological contributions to algorithm auditing