The Future of Work in the Age of AI: Displacement or Risk-Shifting?
The Future of Work in the Age of AI: Displacement or Risk-Shifting? Pegah Moradi and Karen Levy The Oxford Handbook of Ethics of AI Edited by Markus D. Dubber, Frank Pasquale, and Sunit Das Print Publication Date: Jul 2020 Subject: Law, IT and Communications Law Online Publication Date: Jul 2020 DOI: 10.1093/oxfordhb/9780190067397.013.17
Abstract and Keywords This chapter examines the effects of artificial intelligence (AI) on work and workers. As AI-driven technologies are increasingly integrated into workplaces and labor processes, many have expressed worry about the widespread displacement of human workers. The chapter presents a more nuanced view of the common rhetoric that robots will take over people’s jobs. We contend that economic forecasts of massive AI-induced job loss are of limited practical utility, as they tend to focus solely on technical aspects of task execution, while neglecting broader contextual inquiry about the social components of work, organi zational structures, and cross-industry effects. The chapter then considers how AI might impact workers through modes other than displacement. We highlight four mechanisms through which firms are beginning to use AI-driven tools to reallocate risks from them selves to workers: algorithmic scheduling, task redefinition, loss and fraud prediction, and incentivization of productivity. We then explore potential policy responses to both dis placement and risk-shifting concerns. Keywords: artificial intelligence, AI-driven technologies, workplaces, labor processes, displacement, human work ers, AI-induced job loss, task execution, algorithmic scheduling, fraud prediction
IN February 2011, Jeopardy! viewers watched as the AI system known as IBM Watson de feated Ken Jennings and Brad Rutter, two of the winningest Jeopardy! champions of all time, in a three-day exhibition match The New York Times lauded as “a vindication for the academic field of artificial intelligence.”1 Watson’s ability to understand and respond to Jeopardy! clues was considered a major step forward for natural language processing and information retrieval, and soon after, IBM announced plans to use the system to assist physicians in making diagnoses or treating patients.2 Winning at Jeopardy! was a unique challenge for a machine, given that Jeopardy! is more unpredictable and complex than a simple test of trivia; as Jennings wrote in 2019, its clues are “weird, short little haikus, laced with hints, puns, winks, and red herrings.”3 When Watson erred, it often seemed to miss clues that humans would find easy or obvi ous. Watson, for example, rendered “what is chic?” in response to the clue “stylish ele Page 1 of 20
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