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

Abstract

Background: Applications of Healthcare Artificial Intelligence (HCAI) have the potential to improve aspects of healthcare. However, studies have shown that HCAI also has the potential to perpetuate existing inequities, performing less effectively for marginalised populations. Studies on public attitudes toward Artificial Intelligence (AI) outside of the healthcare field have shown higher levels of support for AI amongst socioeconomically advantaged groups that are less likely to be sufferers of algorithmic harms.

Aims: We aimed to examine the sociodemographic predictors of support for scenarios related to HCAI.

Methods: The AVA-AI survey was conducted in March 2020 to assess Australians’ attitudes toward artificial intelligence 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 SEIFA index, the same variables were not predictors of support for the HCAI scenarios presented. Variables associated with support for HCAI across all three scenarios included being male, having computer science or programming experience, and being aged between 18 and 34 years. Other Australian studies suggest that this group have a higher level of perceived familiarity with AI

Conclusions: 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 HCAI.