The Data Center, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430016, Hubei, China.
Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou, 510080, Guangdong, China.
Nat Commun. 2024 Oct 10;15(1):8767. doi: 10.1038/s41467-024-52930-1.
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.
不公平和不平等问题对人工智能（AI）在医疗保健环境中的成功部署构成了严峻挑战。在 AI 模型中，受保护群体之间的表现不平等可能部分归因于对敏感属性和与疾病相关信息之间的虚假或其他不良相关性的学习。在这里，我们引入了属性中立框架，旨在将有偏差的属性与疾病相关信息分离，并对其进行中和处理，以改善不同亚组的代表性。在该框架内，我们开发了属性中立化器（AttrNzr）来生成中性化数据，在这些数据中，人类或机器学习分类器再也无法轻易预测受保护属性。然后，我们利用这些数据来训练疾病诊断模型（DDM）。与其他不公平缓解算法的比较分析表明，AttrNzr 在降低 DDM 的不公平性的同时，保持了 DDM 的整体疾病诊断性能。此外，AttrNzr 支持同时对多个属性进行中和处理，即使仅在训练阶段使用，而不在测试阶段使用，也具有实用性。此外，AttrNzr 并没有对 DDM 引入额外的约束，而是直接解决了不公平问题的根本原因，提供了一种与模型无关的解决方案。我们使用 AttrNzr 的结果突出了以数据为中心和与模型无关的解决方案在 AI 支持的医疗系统中的公平性挑战方面的潜力。
