On Algorithmic Wage Discrimination

Authors:
Veena Dubal, University of California, College of the Law, San Francisco

Abstract:

Recent technological developments related to the extraction and processing of data have given rise to widespread concerns about a reduction of privacy in the workplace. For a growing number of low-income and subordinated racial minority workforces in the United States, however, on-the-job data collection and algorithmic decision-making systems are having a much more profound yet overlooked impact: these technologies are fundamentally altering the experience of labor and undermining the possibility of economic stability and mobility through work. Drawing on a multi-year, first-of-its-kind ethnographic study of organizing on-demand workers, this Article examines the historical rupture in wage calculation, coordination, and distribution arising from the logic of informational capitalism: the use of granular data to produce unpredictable, variable, and personalized hourly pay. Rooted in worker on-the-job experiences, I construct a novel framework to understand the ascent of digitalized variable pay practices, or the transferal of price discrimination from the consumer to the labor context, what I identify as algorithmic wage discrimination. 

Across firms, the opaque practices that constitute algorithmic wage discrimination raise central questions about the changing nature of work and its regulation under informational capitalism. Most centrally, what makes payment for labor in platform work fair? How does algorithmic wage discrimination change and affect the experience of work? And, considering these questions, how should the law intervene in this moment of rupture? 

To preface an assessment, Part I examines the rise of algorithmic wage discrimination and its historic legalization in California and Washington state as crucial occasions to understand how data from labor and algorithmic decision-making systems are changing wage practices in service and logistics sectors. The section also considers the extent to which these new laws comport with legal and cultural expectations about moral economies of work arising from and embedded in longstanding wage equalization statutes—namely, minimum wage and anti-discrimination laws. Part II uses findings and analysis from ethnographic research to assess how data from labor is used to produce algorithmic wage discrimination in ride-hail work and how workers subjectively experience and respond to the practice. I find that workers describe the variable payment structures as forms of gambling and trickery, and that these experiences, in turn, produce profoundly unsettling moral expectations about work and remuneration. Part III assesses both how workers’ groups have leveraged existing data privacy and business association laws to contest algorithmic wage discrimination and the limitations of these approaches. The Article concludes by proposing a non-waivable legal restriction on its practice, which will in turn also restrict harmful data extraction and deter firm fissuring practices.