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

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES BMJ Health & Care Informatics Pub Date : 2023-05-01 DOI:10.1136/bmjhci-2022-100714
Emma Kellie Frost, Pauline O'Shaughnessy, David Steel, Annette Braunack-Mayer, Yves Saint James Aquino, Stacy M Carter
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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.

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社会经济优势的措施不是支持医疗人工智能的独立预测因素:澳大利亚全国调查的亚组分析。
目标:人工智能(AI)的应用具有改善医疗保健方面的潜力。然而,研究表明,医疗保健人工智能算法也有可能使医疗保健领域现有的不平等现象永久化,对边缘人群的效果较差。关于医疗保健领域以外的公众对人工智能的态度的研究往往表明,社会经济优势群体对人工智能的支持程度更高,这些群体不太可能受到算法危害的影响。我们的目的是研究支持与医疗人工智能相关的场景的社会人口学预测因素。方法:于2020年3月进行“澳大利亚人对人工智能的价值观和态度”调查,评估澳大利亚人对人工智能在医疗保健中的态度。一种创新的加权方法涉及将基于网络的非概率小组与分发给具有代表性的澳大利亚人样本的较短综合调查的结果进行加权。我们使用多项逻辑回归来检验各种医疗方案中对人工智能的支持与一系列社会人口变量之间的关系。结果:虽然对人工智能的支持是通过社会经济优势(如教育、家庭收入和地区社会经济指数)来预测的,但这些变量并不能预测对所提出的医疗人工智能方案的支持。与支持医疗人工智能相关的变量包括男性、具有计算机科学或编程经验、年龄在18至34岁之间。澳大利亚的其他研究表明,这些群体可能对人工智能的熟悉程度更高。结论:我们的研究结果表明,虽然对人工智能的总体支持可以通过社会优势指标来预测,但这些指标并不能预测对医疗保健人工智能的支持。
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来源期刊
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
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