Who should decide how limited healthcare resources are prioritized? Autonomous technology as a compelling alternative to humans

TL;DR

The paper explores whether autonomous technology or humans should prioritize limited healthcare resources, finding that people prefer technology when it performs well.

Contribution

The study introduces a novel framework for understanding public perception of autonomous systems in healthcare decision-making.

Findings

Autonomous computer programs are perceived as more bias-free and error-free compared to human staff.

Better performance of autonomous systems can shift public preference toward using them for resource allocation.

Preferences for decision-makers depend on the perceived qualities of agency, emotional experience, bias-free, and error-free.

Abstract

Who should decide how limited resources are prioritized? We ask this question in a healthcare context where patients must be prioritized according to their need and where advances in autonomous artificial intelligence-based technology offer a compelling alternative to decisions by humans. Qualitative (Study 1a; N = 50) and quantitative (Study 1b; N = 800) analysis identified agency, emotional experience, bias-free, and error-free as four main qualities describing people’s perceptions of autonomous computer programs (ACPs) and human staff members (HSMs). Yet, the qualities were not perceived to be possessed equally by HSMs and ACPs. HSMs were endorsed with human qualities of agency and emotional experience, whereas ACPs were perceived as more capable than HSMs of bias- and error-free decision-making. Consequently, better than average (Study 2; N = 371), or relatively better (Studies 3, N = 181; & 4, N = 378), ACP performance, especially on qualities characteristic of ACPs, was sufficient to reverse preferences to favor ACPs over HSMs as the decision makers for how limited healthcare resources should be prioritized. Our findings serve a practical purpose regarding potential barriers to public acceptance of technology, and have theoretical value for our understanding of perceptions of autonomous technologies.

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