"Robots and Organization Studies: Why Robots Might Not Want to Steal Your Job"

by Fleming, Peter (2019)

Abstract

A number of recent high-profile studies of robotics and artificial intelligence (or AI) in economics and sociology have predicted that many jobs will soon disappear due to automation, with few new ones replacing them. While techno-optimists and techno-pessimists contest whether a jobless future is a positive development or not, this paper points to the elephant in the room. Despite successive waves of computerization (including advanced machine learning), jobs have not disappeared. And probably won?t in the near future. To explain why, some basic insights from organization studies can make a contribution. I propose the concept of ?bounded automation? to demonstrate how organizational forces mould the application of technology in the employment sector. If work does not vanish in the age of AI, then poorly paid jobs will most certainly proliferate, I argue. Finally, a case is made for the scholarly community to engage with wider social justice concerns. This I term public organization studies.

Key Passage

I do not want to imply that machines will have no influence on work in the future. Indeed, the impact may be significant, including unemployment. However, the now prevalent forecast of mass joblessness is unlikely to be realized given how AI and digitalization are constrained by socioeconomic and organizational forces that shape its implementation (namely, labour pricing, extant power relations and the job task in question). Furthermore, the concept of bounded automation allows us to understand why increasingly low-skilled (be they unautomated or semi-automated) jobs are likely to flourish while so-called good ones become ever more difficult to acquire. (p.31)

Keywords

Robots, Job Destruction, Technological Unemployment, Artificial Intelligence, Automation, Technology

Themes

Robots, Automation

Links to Reference

• https://doi.org/10.1177/0170840618765568
• http://dx.doi.org/10.1177/0170840618765568
• https://journals.sagepub.com/doi/abs/10.1177/0170840618765568
• https://journals.sagepub.com/doi/pdf/10.1177/0170840618765568

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