Who Benefits? Knowledge Economy Winner and Losers’ Perceptions of Generative AI

• In Event: AI, Automation, and Political Attitudes

Abstract

The divide between winners and losers of the new knowledge economy is arguably the central faultline of contemporary politics in advanced industrial countries. Recent developments in generative artificial intelligence (AI) are concerning for their potential to amplify this divide, by further complementing the skills of already well-positioned knowledge economy workers. At the same time, emerging research compellingly suggests such technologies could instead narrow (some) inequalities, by raising the performance of novice and less-skilled workers more than highly-skilled workers within occupational groups. In this context, and building on previous research showing that support for mainstream and populist parties is influenced by how people interpret the threat of workplace technological adoption (both for their own labour market prospects and for those of their communities), this paper asks: how do people reason about the effects of these new generative AI technologies on labour markets? Who do those who are better- and worse-positioned in the new knowledge economy perceive will be the beneficiaries of these technologies, and, moreover, can providing them with information about the possible benefits for certain groups of workers shift their attitudes? Finally, do perceptions of technology's beneficiaries shape policy preferences in response to generative AI? We answer these questions by drawing on two pre-registered survey experiments of respondents in Canada and the United States. Findings suggest that "winners" of the new knowledge economy (especially more educated voters) are more optimistic about technology's potential benefits for the economy and society, but respondents are also sensitive to perceptions of who benefits from these new technologies.

Authors

• Sophie Borwein, University of British Columbia

• Beatrice Magistro, California Institute of Technology

• R. Michael Alvarez, California Institute of Technology

• Bart Bonikowski, New York University

• Peter Loewen, University of Toronto