FAI: Foundations of Fair AI in Medicine: Ensuring the Fair Use of Patient Attributes

机器学习模型支持影响美国医疗保健系统中数百万患者的决策，包括诊断疾病、促进急诊室的分诊以及为重症监护室的监督提供信息。在此类应用中，模型通常包括年龄、体重和就业状况等组属性，以捕获患者亚组之间的差异。构建具有组属性的模型的标准技术通常提高整个患者群体的总体性能。然而，这样的模式可能会导致特定群体的表现更差。在这种情况下，该模型可能会为这些群体分配可预防的不准确预测，从而破坏医疗保健和健康结果。该项目旨在通过开发工具来防止这种伤害，以确保在预测模型中公平使用组属性。目标是确保模型使用组属性的方式为每个组产生定制的性能优势。目前在医学中部署的机器学习模型可能会表现出损害健康结果的合理使用违规行为。该项目减轻了在医学中部署机器学习的关键阶段的合理使用违规行为：验证，模型开发和通信。首先，它开发工具来检查模型是否确保合理使用。这些工具包括理论保证，用于描述模型开发的常见方法何时会产生违反合理使用的行为，以及用于验证模型在部署之前和部署期间是否违反合理使用的统计测试。其次，它开发了具有合理使用保证的学习模型的算法。算法将针对医学中的突出用例进行量身定制，与开源软件配对，并应用于为现实世界的医疗应用构建决策支持工具。第三，它创建了工具来告知关键利益相关者（监管机构，医生和患者）关于模型的合理使用保证。该项目借鉴了机器学习、信息论、优化、以人为本的设计以及在临床环境中部署模型的专业知识。通过与医学研究人员和工业界的合作，确保在医学中公平使用群体属性的工具包将嵌入到现实世界的系统中。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Machine learning models support decisions that affect millions of patients in the U.S. healthcare system in diagnosing illnesses, facilitating triage in emergency rooms, and informing supervision at intensive care units. In such applications, models will often include group attributes such as age, weight, and employment status to capture differences between patient subgroups. Standard techniques to build models with group attributes typically improve aggregate performance across the entire patient population. As a result, however, such models may lead to worse performance for specific groups. In such cases, the model may assign these groups preventable inaccurate predictions that undermine medical care and health outcomes. This project aims to prevent this harm by developing tools to ensure the fair use of group attributes in predictive models. The goal is to ensure that a model uses group attributes in a way that yields a tailored performance benefit for every group. Currently deployed machine learning models in medicine may exhibit fair use violations that undermine health outcomes. This project mitigates fair use violations at key stages in the deployment of machine learning in medicine: verification, model development, and communication. First, it develops tools to check if a model ensures fair use. These tools include theoretical guarantees that characterize when common approaches to model development produce fair use violations, and statistical tests to verify if a model violates fair use before and during deployment. Second, it develops algorithms for learning models with fair use guarantees. Algorithms will be tailored for salient use cases in medicine, paired with open-source software, and applied to build decision support tools for real-world medical applications. Third, it creates tools to inform key stakeholders (regulators, physicians, and patients) about a model's fair use guarantees. The project draws on machine learning, information theory, optimization, human-centered design, as well as expertise in deploying models in clinical settings. The resulting toolkit for ensuring fair use of group attributes in medicine will be embedded in real-world systems through collaborations with medical researchers and industry.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.