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Are robots / AI viewed as more of a workforce threat in unequal societies? Evidence from the Eurobarometer survey
Shoss, Mindy K. Ciarlante, Katherine
Shoss, Mindy K.
Ciarlante, Katherine
Abstract
Although advanced technologies (i.e., artificial intelligence [AI], robots) are often discussed as drivers of societal inequality, our research examines whether people living in more unequal societies tend to view technology as a greater threat to jobs in general. Building from research that societal inequality heightens concerns about status hierarchies and future resource attainment, we anticipated that workers in more unequal societies would tend to view AI/robots as greater threats (e.g., AI/robots as job destroyers). Utilizing the Eurobarometer 87.1 data set, we found that country inequality, as operationalized via the Gini index, was positively associated with perceptions that AI/robots pose threats of general job loss. These relationships occurred when controlling for people’s perceptions of technological threat to their own personal job, technology skills and interests, and demographics. Moreover, these findings are robust across alternative operationalizations of inequality including the Human Inequality Index and people’s subjective perceptions of current and future inequality in their country. These findings advance theory on inequality and suggest that the broader context—both objective and perceived—may play a role in how people view disruption associated with AI/robots at work.
Keywords
inequality, robots, AI, threat perception
Date
2022
Type
Journal article
Journal
Book
Volume
3
Issue
2
Page Range
1-13
Article Number
ACU Department
Collections
Relation URI
Event URL
Open Access Status
Open access
License
CC BY-NC-ND 4.0
File Access
Open
Notes
This work is licensed under a Creative Commons
Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND). This license permits copying and redistributing
the work in any medium or format for noncommercial use provided the original authors and source are credited and a link to the license is included in attribution. No derivative works are permitted under this
license.
Funding: This article was supported by Grant T42OH008438, funded by the National Institute for Occupational Safety and Health (NIOSH) under the Centers for Disease Control and Prevention (CDC). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIOSH or CDC or the Department of Health and Human Services.
Data Availability: Data for this study are publicly available. Links to data, study materials, output, and analytical code are available on an Open Science Foundation page for this project at https://osf.io/r9qap/?view_only=96d0 d3393fab4f6682544a1ec692dca8.
The data are available at https://osf.io/r9qap/?view_only=96d0d3393fab 4f6682544a1ec692dca8. The experimental materials are available at https://osf.io/r9qap/?view_ only=96d0d3393fab4f6682544a1ec692dca8.
Funding: This article was supported by Grant T42OH008438, funded by the National Institute for Occupational Safety and Health (NIOSH) under the Centers for Disease Control and Prevention (CDC). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIOSH or CDC or the Department of Health and Human Services.
Data Availability: Data for this study are publicly available. Links to data, study materials, output, and analytical code are available on an Open Science Foundation page for this project at https://osf.io/r9qap/?view_only=96d0 d3393fab4f6682544a1ec692dca8.
The data are available at https://osf.io/r9qap/?view_only=96d0d3393fab 4f6682544a1ec692dca8. The experimental materials are available at https://osf.io/r9qap/?view_ only=96d0d3393fab4f6682544a1ec692dca8.