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On May 3, 2023, the White House Office of Science and Technology Policy (OSTP) requested public input on automated worker surveillance and management systems.

In this Comment, the Knowing Machines Research Project (Knowing Machines) urges OSTP to translate workers’ expectations of privacy in their data into guidance for employers on when and what types of data they can collect. As worker data fuels automated surveillance technologies by serving as training data for machine-learning models, we encourage OSTP to set a high bar for employers who exploit worker data to inform employment decision and undermine workers’ autonomy. Specifically, we propose OSTP collaborate with other federal agencies to set baseline protections over worker data that align with the limits on health data for healthcare providers (HIPAA) and on consumer financial data for financial institutions respectively. At a minimum, we hope OSTP will adopt clear policies protecting worker data relating to union organizing communications and activities.

Our main point: "There is growing concern about the misuse of data to train machine-learning models powering automated surveillance technologies, and worker data should be no exception. In its consideration of automated worker surveillance systems, OSTP must be mindful of the enclosure of worker data that enables the development of these systems in the first place. The lack of clear privacy protections for worker data provides OSTP a unique opportunity to guide employers on when and what types of worker data they can collect, store, use, and sell. OSTP must act now to counterbalance employers’ insatiable thirst for more comprehensive and invasive worker data to fuel people analytics solutions, bringing autonomy over worker data explicitly into the conversation."