Measures of socioeconomic advantage are not independent predictors of support for healthcare AI: subgroup analysis of a national Australian survey

Emma Kellie Frost; Pauline O'Shaughnessy; David Steel; Annette Braunack-Mayer; Yves Saint James Aquino; Stacy M Carter

doi:10.1136/bmjhci-2022-100714

Measures of socioeconomic advantage are not independent predictors of support for healthcare AI: subgroup analysis of a national Australian survey

BMJ Health Care Inform. 2023 May;30(1):e100714. doi: 10.1136/bmjhci-2022-100714.

Authors

Emma Kellie Frost¹, Pauline O'Shaughnessy², David Steel³, Annette Braunack-Mayer⁴, Yves Saint James Aquino⁴, Stacy M Carter⁴

Affiliations

¹ Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences and Humanities, University of Wollongong, Wollongong, New South Wales, Australia emmaf@uow.edu.au.
² School of Mathematics and Applied Statistics, Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, New South Wales, Australia.
³ Centre for Sample Survey Methodology, National Institute for Applied Statistics Research Australia, School of Mathematics and Applied Statistics, Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, New South Wales, Australia.
⁴ Australian Centre for Health Engagement, Evidence and Values, School of Health and Society, Faculty of the Arts, Social Sciences and Humanities, University of Wollongong, Wollongong, New South Wales, Australia.

Abstract

Objectives: Applications of artificial intelligence (AI) have the potential to improve aspects of healthcare. However, studies have shown that healthcare AI algorithms also have the potential to perpetuate existing inequities in healthcare, performing less effectively for marginalised populations. Studies on public attitudes towards AI outside of the healthcare field have tended to show higher levels of support for AI among socioeconomically advantaged groups that are less likely to be sufferers of algorithmic harms. We aimed to examine the sociodemographic predictors of support for scenarios related to healthcare AI.Methods: The Australian Values and Attitudes toward AI survey was conducted in March 2020 to assess Australians' attitudes towards AI in healthcare. An innovative weighting methodology involved weighting a non-probability web-based panel against results from a shorter omnibus survey distributed to a representative sample of Australians. We used multinomial logistic regression to examine the relationship between support for AI and a suite of sociodemographic variables in various healthcare scenarios.Results: Where support for AI was predicted by measures of socioeconomic advantage such as education, household income and Socio-Economic Indexes for Areas index, the same variables were not predictors of support for the healthcare AI scenarios presented. Variables associated with support for healthcare AI included being male, having computer science or programming experience and being aged between 18 and 34 years. Other Australian studies suggest that these groups may have a higher level of perceived familiarity with AI.Conclusion: Our findings suggest that while support for AI in general is predicted by indicators of social advantage, these same indicators do not predict support for healthcare AI.

Keywords: Artificial intelligence; Delivery of Health Care; Health Equity; Machine Learning.

MeSH terms

Adolescent
Adult
Artificial Intelligence*
Australia
Delivery of Health Care*
Female
Humans
Male
Socioeconomic Factors
Young Adult

Supplementary concepts

Australians