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

FAI：医学中公平人工智能的基础：确保患者属性的公平使用

• 批准号: 2040880
• 负责人: Flavio Calmon
• 金额: $ 62.5万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040880&HistoricalAwards=false
• 关键词: FAI; Foundations; Fair; AI; Medicine; FAI; Foundations; Fair; AI; Medicine

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.

机器学习模型支持决定影响美国医疗保健系统中数百万患者的决策，以诊断疾病，促进急诊室中的分类以及在重症监护病房的监督信息。在此类应用中，模型通常将包括年龄，体重和就业状况等组属性，以捕获患者亚组之间的差异。建立具有组属性的模型的标准技术通常会改善整个患者人群的总绩效。因此，这样的模型可能会导致特定组的性能较差。在这种情况下，该模型可能会分配这些群体可预防的不准确预测，以破坏医疗和健康结果。该项目旨在通过开发工具来确保在预测模型中合理使用组属性，以防止这种危害。目的是确保模型以对每个组产生量身定制的性能益处的方式使用组属性。目前，医学中部署的机器学习模型可能会表现出破坏健康结果的合理使用违规行为。该项目在医学中的机器学习部署：验证，模型开发和沟通时会减轻关键阶段的公平使用违规行为。首先，它开发了检查模型是否确保合理使用的工具。这些工具包括理论保证，表征何时共同建模开发的方法会产生合理的用法违规行为，以及统计测试以验证模型是否违反了部署前后的公平用途。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显着用例量身定制，并与开源软件配对，并应用于为现实世界中医疗应用构建决策支持工具。第三，它创建了工具，以告知主要利益相关者（监管机构，医师和患者）有关模型的合理使用保证。该项目借鉴了机器学习，信息理论，优化，以人为中心的设计以及在临床环境中部署模型方面的专业知识。通过与医学研究人员和行业的合作，将嵌入医学中的小组属性的最终工具包将嵌入现实世界系统中。该奖项反映了NSF的法定任务，并被认为值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估。