Explainable Artificial Intelligence (XAI) Based Analysis of Stress Among Tech Workers Amidst COVID-19 Pandemic

Work stress is now a global concern. Particularly, as a result of the extended lockdown during COVID-19, many industries, particularly those in the tech industry, were obliged to adopt remote working. In this chapter, we analyze the prevalence of mental health illnesses among tech professionals and assess attitudes toward mental health in the workplace. Question-answer based stress detection is one of the most widely used methods nowadays since it preserves social distance and is effective. Algorithms used in artificial intelligence (AI) are frequently referred to as “black boxes,” due to their lack of explainability nature. Again, an AI paradigm called Explainable AI (XAI) aims to make end users aware of the goals, choices, and rationale behind the system. End users may be anybody whose decisions are affected by an AI model, including consumers, data scientists, regulatory authorities, domain experts, executive board members, and managers who employ AI with or without knowledge. The goal of this research is to make a system that is very flexible and uses XAI to find the stressors at work that hurt the mental health of tech professionals. As per the best belief of the authors, till now, no researcher has reported in this domain. The findings point to both qualitative and quantitative visual representations that might provide doctors additional in-depth information about the results provided by the learned XAI models, enhancing their understanding and decision-making.