Impacts of generative artificial intelligence on the future of labor market: A systematic review - Nader Salari; Mahan Beiromvand; Amin Hosseinian-Far; Javad Habibi; Fateme Babajani; Masoud Mohammadi (Computers in Human Behavior Reports)
Background: Generative AI (GenAI) has the ability to autonomously collect and process data to generate contents, inform decisions, solve problems, and perform tasks that typically require human reasoning. This Systematic Review is conducted to examine the impacts of GenAI on the future of employment, focusing on concerns about rising unemployment, and the positive and negative perspectives outlined within exiting studies. The findings from this review can help identify research gaps, guide organizational planning, and improve AI governance frameworks and policies. Methods: To identify relevant studies, the PubMed, Scopus, Web of Science, Embase, ScienceDirect and Google Scholar databases and repositories were systematically searched using the keywords: ‘Future of work’, ‘Job market’, ‘Generative AI’, ‘Generative AI’, and ‘ChatGPT’. Additionally, the reference lists of the identified related articles were reviewed for grey literature. Results: Following the PRISMA guidelines, a total of 14 articles were selected for analysis. Selected studies have examined the positive and negative viewpoints on GenAI, together with pertinent challenges and opportunities. Accordingly, GenAI, when compliant with security and ethical issues, has the potential to increase efficiency whilst reducing costs and time. Conclusion: Considering the rapid growth and adoption of AI technologies, examining the impacts of GenAI on the future of labor market is crucial. GenAI is likely to create new roles in some sectors yet reduce opportunities in others. A nuanced assessment of the impacts, and ongoing monitoring are vital for effective preparation and adaptation to the evolving work landscape in the presence of advanced AI technologies.