Enhancing fairness in AI-enabled medical systems with the attribute neutral framework.

ABSTRACT: Questions of unfairness and inequity pose critical challenges to the successful deployment of artificial intelligence (AI) in healthcare settings. In AI models, unequal performance across protected groups may be partially attributable to the learning of spurious or otherwise undesirable correlations between sensitive attributes and disease-related information. Here, we introduce the Attribute Neutral Framework, designed to disentangle biased attributes from disease-relevant information and subsequently neutralize them to improve representation across diverse subgroups. Within the framework, we develop the Attribute Neutralizer (AttrNzr) to generate neutralized data, for which protected attributes can no longer be easily predicted by humans or by machine learning classifiers. We then utiliz

SUBMITTER: Hu L 

PROVIDER: S-EPMC11464531 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature