Linking artificial intelligence job exposure to expectations: Understanding AI losers, winners, and their political preferences

The rapid expansion of Artificial Intelligence (AI) in the workplace has significant political implications. How can we understand perceptions of both personal job risks and opportunities, given each may affect political attitudes differently? We use an original, representative survey from Great Britain to reveal; (i) the degree to which people expect personal AI-based occupational risks versus opportunities, (ii) how much this perceived exposure corresponds to variation in existing expert-derived occupational AI-exposure measures; (iii) the social groups who expect to be AI winners and AI losers; and (iv) how personal AI expectations are associated with demand for different political policies. We find that over 1-in-3 British workers anticipate being an AI winner (10%) or loser (24%) and, while expectations correlate with classifications of occupational exposure, factors like education, gender, age, and employment sector also matter. Politically, both self-anticipated AI winners and losers show similar support for redistribution, but they differ on investment in education and training as well as on immigration. Our findings emphasise the importance of considering subjective winners and losers of AI; these patterns cannot be explained by existing occupational classifications of AI exposure.

Authors

Institutions

Publication Details

Journal
Research & Politics
Published
2025-04-01
DOI
https://doi.org/10.1177/20531680251337897
Primary Topic
Digital Economy and Work Transformation
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Linking artificial intelligence job exposure to expectations: Understanding AI losers, winners, and their political preferences

Zack Grant, Geoffrey M. Evans, Jane Green, Gaetano Inglese
Research & Politics
Digital Economy and Work Transformation
article

Linking artificial intelligence job exposure to expectations: Understanding AI losers, winners, and their political preferences

Zack Grant, Geoffrey M. Evans, Jane Green, Gaetano Inglese
article en

Abstract

The rapid expansion of Artificial Intelligence (AI) in the workplace has significant political implications. How can we understand perceptions of both personal job risks and opportunities, given each may affect political attitudes differently? We use an original, representative survey from Great Britain to reveal; (i) the degree to which people expect personal AI-based occupational risks versus opportunities, (ii) how much this perceived exposure corresponds to variation in existing expert-derived occupational AI-exposure measures; (iii) the social groups who expect to be AI winners and AI losers; and (iv) how personal AI expectations are associated with demand for different political policies. We find that over 1-in-3 British workers anticipate being an AI winner (10%) or loser (24%) and, while expectations correlate with classifications of occupational exposure, factors like education, gender, age, and employment sector also matter. Politically, both self-anticipated AI winners and losers show similar support for redistribution, but they differ on investment in education and training as well as on immigration. Our findings emphasise the importance of considering subjective winners and losers of AI; these patterns cannot be explained by existing occupational classifications of AI exposure.

Research & PoliticsVol. 12(2)
Nuffield Foundation (GB), Nuffield Health (GB), London School of Economics and Political Science (GB)
Joseph Rowntree Foundation, Lunds Universitet
Openalex Percentile: Top 84%
Digital Economy and Work Transformation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.