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 utilize these data to train the disease diagnosis model (DDM). Comparative analysis with other unfairness mitigation algorithms demonstrates that AttrNzr outperforms in reducing the unfairness of the DDM while maintaining DDM's overall disease diagnosis performance. Furthermore, AttrNzr supports the simultaneous neutralization of multiple attributes and demonstrates utility even when applied solely during the training phase, without being used in the test phase. Moreover, instead of introducing additional constraints to the DDM, the AttrNzr directly addresses a root cause of unfairness, providing a model-independent solution. Our results with AttrNzr highlight the potential of data-centered and model-independent solutions for fairness challenges in AI-enabled medical systems.