Mitigating the impact of biased artificial intelligence in emergency decision-making.

ABSTRACT:

Background

Prior research has shown that artificial intelligence (AI) systems often encode biases against minority subgroups. However, little work has focused on ways to mitigate the harm discriminatory algorithms can cause in high-stakes settings such as medicine.

Methods

In this study, we experimentally evaluated the impact biased AI recommendations have on emergency decisions, where participants respond to mental health crises by calling for either medical or police assistance. We recruited 438 clinicians and 516 non-experts to participate in our web-based experiment. We evaluated participant decision-making with and without advice from biased and unbiased AI systems. We also varied the style of the AI advice, framing it either as prescriptive recommendations or descripti

SUBMITTER: Adam H 

PROVIDER: S-EPMC9681767 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature