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Collecting information about one group can facilitate control over an entirely different group—a phenomenon we term refractive surveillance. We explore this dynamic in the context of retail stores by investigating how retailers’ collection of data about customers facilitates new forms of managerial control over workers. We identify four mechanisms through which refractive surveillance might occur in retail work, involving dynamic labour scheduling, new forms of evaluation, externalization of worker knowledge, and replacement through customer self-service. Our research suggests that the effects of surveillance cannot be fully understood without considering how populations might be managed on the basis of data collected about others.

 ³ÉÈË´óƬ the Speaker:  Solon Barocas is an Assistant Professor in the Department of Information Science at Cornell University. His current research explores ethical and policy issues in artificial intelligence, particularly fairness in machine learning, methods for bringing accountability to automated decision-making, and the privacy implications of inference. In 2014, Dr.Barocas co-founded Fairness, Accountability, and Transparency in Machine Learning (FAT/ML), an annual event that brings together researchers working on these issues. Solon Barocas was previously a Postdoctoral Researcher at Microsoft Research,  as well as a Postdoctoral Research Associate at the Center for Information Technology Policy at Princeton University.

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