Affective Computing at Work: Rationales for Regulating Emotion Attribution and Manipulation in the Workplace – Dr. Frank Pasquale
October 24, 2023
11:00 AM - 1:00 PM
601 S. Morgan St., Chicago, IL 60607
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Affective computing is the computer science field’s term for attempts to read, simulate, predict, and stimulate human emotion with software. Advocates for affective computing in the workplace claim that it can improve efficiency, identify better and worse work styles, and indicate how engaged employees are. While these are understandable aspirations, affective computing also raises many ethical and policy concerns, especially when it enters workplaces already highly influenced by algorithmic management.
Affective computing has become a popular computational and psychological research program. Teams are now programming robots, chatbots, and animations to appear to express sadness, empathy, curiosity, and much more. Automated face analysis is translating countless images of human expressions into code that results in standardized classifications and responses by machines. As affective computing is slowly adopted in the workplace, it will increasingly judge workers and try to manipulate them. At least four concerns about algorithmic management via affective computing will be described in this talk: misrecognition, privacy invasion, modulation, and alienation. Potential legal and policy responses will also be explored.
Bio: Frank Pasquale is Professor of Law at Cornell Law School and Cornell Tech. His books include The Black Box Society (Harvard University Press, 2015) and New Laws of Robotics (Harvard University Press, 2020). He has published more than 70 journal articles and book chapters, and co-edited The Oxford Handbook on the Ethics of Artificial Intelligence (Oxford University Press, 2020) and Transparent Data Mining for Big and Small Data (Springer-Verlag, 2017). He has held chaired professorships at the University of Maryland, Seton Hall University, and Brooklyn Law School. He has also served as a distinguished visiting faculty member at the University of Toronto Faculty of Law, visiting professor at Yale Law School, and visiting fellow at Princeton’s Center for Information Technology Policy. His work on “algorithmic accountability” has helped bring the insights and demands of social justice movements to AI law and policy. In privacy law and surveillance, his work is among the leading legal research on regulation of algorithmic ranking, scoring, and sorting systems. He has also written extensively on internet platform regulation.
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