Abstract:
Abstract
The rapid advancement of artificial intelligence (AI) technologies is transforming various sectors, including manufacturing, healthcare, education, and finance. As AI capabilities rapidly improve, their impact on the workforce has emerged as a critical area of research. The increased integration of AI into discursive reports warrants a critical examination of how AI is framed within organisational discourses on graduate employability and the future of work. Informed by the guiding role of policy from key international organisations in AI development, governance, and use, this study explores how AI technologies are positioned in relation to graduate employability and the future of work in policy documents from global entities (e.g., OECD, ILO, and UNESCO). Utilising the concept of interpretive flexibility from the Social Construction of Technology (SCOT) theory, we adapt its principles to document analysis, recognising that policy briefs, technical reports, and working papers reflect the perspectives and interpretations of various stakeholders involved in the AI discourse. Through discourse analysis, we identify dominant representations of AI, understand the effects of these representations, and uncover the overlooked aspects of AI in these discussions. We delineate two primary discourses: AI as a tool for economic growth, reflecting perspectives attributed to policymakers and tech companies, and AI as a cause of job displacement, typically associated with labour unions and workers. The study also highlights the limited representation that attributes increased inequality in employment primarily to AI. We argue that AI, as a technology, does not inherently extend employment inequality; instead, such inequalities stem from how AI is implemented and the broader social, cultural, and economic contexts and regulatory frameworks in which it operates. This paper provides implications for policymakers, employers, and higher education institutions to recognise the socially situated nature of AI in employment policy discourses, and to consider ways that prepare the workforce to navigate the nexus of AI and broader societal changes in the future of work.
References
Douglas, D. G. (2012). The social construction of technological systems, anniversary edition: New directions in the sociology and history of technology. MIT press.
The rapid advancement of artificial intelligence (AI) technologies is transforming various sectors, including manufacturing, healthcare, education, and finance. As AI capabilities rapidly improve, their impact on the workforce has emerged as a critical area of research. The increased integration of AI into discursive reports warrants a critical examination of how AI is framed within organisational discourses on graduate employability and the future of work. Informed by the guiding role of policy from key international organisations in AI development, governance, and use, this study explores how AI technologies are positioned in relation to graduate employability and the future of work in policy documents from global entities (e.g., OECD, ILO, and UNESCO). Utilising the concept of interpretive flexibility from the Social Construction of Technology (SCOT) theory, we adapt its principles to document analysis, recognising that policy briefs, technical reports, and working papers reflect the perspectives and interpretations of various stakeholders involved in the AI discourse. Through discourse analysis, we identify dominant representations of AI, understand the effects of these representations, and uncover the overlooked aspects of AI in these discussions. We delineate two primary discourses: AI as a tool for economic growth, reflecting perspectives attributed to policymakers and tech companies, and AI as a cause of job displacement, typically associated with labour unions and workers. The study also highlights the limited representation that attributes increased inequality in employment primarily to AI. We argue that AI, as a technology, does not inherently extend employment inequality; instead, such inequalities stem from how AI is implemented and the broader social, cultural, and economic contexts and regulatory frameworks in which it operates. This paper provides implications for policymakers, employers, and higher education institutions to recognise the socially situated nature of AI in employment policy discourses, and to consider ways that prepare the workforce to navigate the nexus of AI and broader societal changes in the future of work.
References
Douglas, D. G. (2012). The social construction of technological systems, anniversary edition: New directions in the sociology and history of technology. MIT press.