“The Political Economy of Low-Wage Worker Datafication,” Seema N. Patel, UC Law San Francisco

A rapid digital transformation is underway in low-wage workplaces, where employers deploy artificial intelligence, algorithms, and other emerging technologies to track everything from a hotel worker’s body temperature (biometric data) to the nuances of their cleaning patterns (geolocation), to the facial expressions and tone (audio data) of fast-food workers as they interact with customers. The result is troves of extracted data used not only to manage and control job performance but to accrue distinct value—often without workers’ knowledge yet within the purview of managerial prerogative. As the World Economic Forum has observed, workers have little agency over data collected during employment, largely due to technological complexity, lack of transparency, and inadequate legal protections. This phenomenon—the datafication of low-wage workers—raises serious concerns, and expansive questions, about their privacy rights as biometric, personal, and performance data is collected, processed, and sold without oversight or consent. While low-wage workers’ data are wielded to train, test, and refine employer-controlled machine learning models, datasets can also be shared or sold on the open market. Within a market economy in which, by some assessments, the value of data exceeds that of even oil or gold, this project asks: what is the emerging political economy of low-wage worker datafication?

For the 26 million low-wage workers in the U.S.—disproportionately immigrants, women, and people of color—data rights are virtually nonexistent. Scholars have examined how platform companies wield algorithms to oppress workers, the political economy of broad-based workplace technological change, and examined how U.S. privacy law is insufficient to address these issues. One scholar describes the legal landscape as an awkward split between privacy and intellectual property rights, with workers losing out on both ends. Others sound the alarm about data mining practices that enable employers and other entities to infer personal and sensitive information from workers beyond what is directly observable.

Professor Seema N. Patel introduces a new account of the political economy of low-wage worker datafication, showing how gaps, misalignments, and legal doctrine actively facilitate the large-scale extraction, governance, and commodification of low-wage worker data. It explores what (if any) rights workers might claim in the data generated through their labor, and whether current legal frameworks—rooted primarily in individual contracts and managerial prerogative—offer any recourse in this technological era. Absent meaningful guardrails or governance mechanisms, the extraction and exploitation of worker data risks deepening existing power imbalances, with profound implications for privacy, dignity, and autonomy—and for the broader political economy.

Professor Patel’s research and scholarship cover a wide range of fields, from work law and technology, social movements, employment law, and labor law to state and local government law and administrative law. Combining legal analysis with qualitative research methodologies, her research captures the economic and socio-legal needs of low-wage workers, including the human dynamics of the state-worker relationship, the effect of workplace technological developments on economic policy and the workforce, and the role of institutional and individual actors in that project. Drawing on her years of litigation, policy advocacy, organizing, and teaching experience, including extensive collaboration with workers, labor unions, and worker centers, Professor Patel’s research informs critical interventions that address the rapidly changing landscape of work law. Her work has been published in California Law Review, Harvard Law & Policy Review, Maryland Law Review, and the Berkeley Journal of Employment & Labor Law (BJELL).