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

Applications of AI (artificial intelligence) 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 toward AI outside of the healthcare field have tended to show higher levels of support for AI amongst 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. 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. 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 scenarios presented. Variables associated with support for healthcare AI 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. 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. WHAT IS ALREADY KNOWN ON THIS TOPIC Artificial intelligence has the potential to perpetuate existing biases in healthcare datasets, which may be more harmful for marginalised populations. Support for the development of artificial intelligence tends to be higher amongst more socioeconomically privileged groups. WHAT THIS STUDY ADDS Whilst general support for the development of artificial intelligence was higher amongst socioeconomically privileged groups, support for the development of healthcare artificial intelligence was not. Groups that were more likely to support healthcare artificial intelligence were males, those with computer science experience, and younger people. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE, OR POLICY Healthcare artificial intelligence is becoming more relevant for the public as new applications are developed and implemented. Understanding how public attitudes differ amongst sociodemographic subgroups is important for future governance of healthcare AI. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by the University of Wollongong's Global Challenges program. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: the Human Research Ethics Committee at the University of Wollongong gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Due to ethical restrictions, data for this manuscript are not available.