Facial Emotion Recognition in the Future of Work: Social Implications and Policy Recommendations

Abstract

Facial biometric systems potentially allow for the overt and covert detection of a person for a range of use case scenarios. This article considers a human resource management (HRM) workplace scenario where employees are monitored through cameras on personal electronic devices for the purposes of facial emotion recognition. The applications described pertain broadly to the “future of work” context. The article considers how employers, would use employee facial emotion data for data-driven decision-making in, for example, the construction and optimization of virtual teams, appropriateness for promotion to leadership positions, and fitness-to-task in mission critical work. Building on the outcomes of a socio-technical study, the initial component of which was an FER prototype, this paper considers the social implications and policy recommendations of the deployment of the technical system. Findings indicate that coded biases in determinations of FER include possible discrimination against women, racial minorities, undocumented immigrants and refugees, and people with visible and invisible disabilities.