Abstract:

Workplace well-being, that was formerly a mere HR metric has turned out to be a key driver of employee engagement and a compelling force for an organization’s long-term effectiveness and success. Well-being in the workplace can be promoted by employing practices that can strengthen and empower employees both mentally and physically. Companies around the world have started using emotional computing technologies to know, manage and monitor employees’ emotional states. ‘Emotion AI’, otherwise called as affective computing is a popular AI-driven tool to detect and interpret human emotions. Affective computing finds its applicability in the human side of workplace due to its potential in recognition, expression, modelling, communication, and response to emotion. The capability of affective computing in gauging human emotions makes it a sophisticated HR tool in the modern workplace. However, several criticisms have been raised against its application in the workplace. The inaccuracies in capturing the emotions, faux emotional manifestations, failure in reflecting the real behaviour of humans and faulty reactiveness are some of the crucial practical challenges in employing affective computing in the workplace. Ethical challenges in implementing affective computing include breach of personal and private space of employees in the workplace. Despite these criticisms, affective computing technologies are increasingly applied for fostering an emotionally healthy workplace. This paper focuses on developing a robust model for Facial Emotion Recognition tailored for Human Resources applications examining theoretically, the practical cum ethical challenges in its implementation. Further, this paper uses efficientnet on emotion dataset to develop HRnet, a deep learning model and appraises its suitability as a HR tool for promoting well-being in the workplace.