RCMI@Morgan: Center for Urban Health Disparities Research and Innovation

项目摘要 非洲血统健康差异的特征和算法偏差的减少 在大数据时代，算法存在于整个社会，将其转变为更加个性化， 奉承。一个主要的融合是生物学，医学和医疗保健算法的应用。 尽管如此，最近的一项研究表明，一种广泛使用的算法，典型的这种行业范围的方法， 影响数百万患者，表现出显着的种族偏见：在给定的风险评分，黑人患者 比白色患者病情严重得多，如不受控制的疾病迹象所证明。这是一个具体的 一个被称为算法偏见的更广泛问题的例子，其中算法恢复了文化偏见 编码在数据集上，它们被训练。越来越多的人认识到算法偏见的影响， 对算法责任的相应呼吁。卡普兰等人将“职业责任制”定义为 是指如何创建算法及其对社会的影响的责任分配;如果损害 发生时，问责系统包括一个补救机制”（2018年：10）。也就是说，算法问责制 在算法设计及其效果中包括危害减少和预防考虑。 这些“算法偏见”和“算法责任”的问题也涉及非洲人的健康差异 祖先暴露在父母RCMI奖与以下目标：（1）提高密歇根州立大学的健康差距 基础生物医学和行为/公共卫生研究的研究基础设施和能力，（2） 加强关于城市健康和健康差距的高质量研究，包括转化研究， 增加外部资金、出版物和对社区的科学服务，（3）促进合作 在基础生物医学和社会/行为学院研究人员之间， 教师职业发展的环境，特别是对新的和早期的职业教师和（4）建立 与两个研究密集型机构，约翰霍普金斯大学和大学的可持续伙伴关系 马里兰州，巴尔的摩，以及当地政府和社区组织处理卫生 差距。 在本项目的目标1中，将使用以下统计数据重新定义父RCMI奖的当前指标： 在性别、种族和社会经济地位的频谱中的概率分布空间。潜在数据偏倚 将在表征“算法偏差”之前被过滤掉。根据重新定义的指标， 目标1，考虑到广泛的人群，人工智能技术将向精准医学方向发展 和血统来减少已识别的偏见。具体来说，将开发推理和学习算法 概率空间上的元学习通过贝叶斯优化与正则化。 我们将举办公开研讨会和工作坊，并进行案例研究，涵盖以下方面的互动主题： 伦理、人工智能和健康差异。分析框架和工具将公开传播， 可扩展性和可集成性与公共门户分析“我们所有人。"

Project Summary Characterization of health disparities in African ancestry and reduction of algorithmic bias In the epoch of big data, algorithms are present throughout society, transforming it into more personalized and flatter. One primary convergence is the application of algorithms for biology, medicine, and health care. Nonetheless, a recent study shows that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. This is a specific example of a broader issue known as algorithmic bias, wherein algorithms reinstate the cultural biases encoded in the data sets they are trained on. Increasing appreciation for the impact of algorithmic bias has led to a corresponding call for algorithmic accountability. Caplan et al. define "Algorithmic accountability ultimately refers to the assignment of responsibility for how an algorithm is created and its impact on society; if harm occurs, accountable systems include a mechanism for redress" (2018: 10). That is, algorithmic accountability includes harm reduction and prevention considerations in both the design of the algorithm and its effects. These issues of “algorithmic bias” and “algorithmic accountability” also involve in health disparities in African ancestry exposed in the parent RCMI award with following goals: (1) Enhance MSU's health disparities research infrastructure and capacity in both basic biomedical and behavioral/public health research, (2) Enhance high-quality research, including translational research, on urban health and health disparities through increased external funding, publications and scientific services to the community, (3) Facilitate collaborations between basic biomedical and social/behavioral faculty researchers and create a collaborative and supportive environment for faculty career development, especially for new and early career faculty and (4) Build sustainable partnerships with two research-intensive institutions, Johns Hopkins University and the University of Maryland, Baltimore, as well as local government and community-based organizations dealing with health disparities. In the Aim 1 for this project, the current metrics of the parent RCMI award will be redefined with statistics on the probability distribution space in the spectra of gender, race, and socioeconomic status. Potential data bias in de-identification will be filtered out before characterizing “algorithmic bias.” Based on the redefined metrics of Aim 1, AI techniques will be advanced toward precision medicine considering the broad spectra of population and ancestry to reduce the identified biases. Specifically, inference and learning algorithms will be developed on probability spaces with meta-learning via Bayesian optimization with regularization. We will host public seminars and workshops with case studies covering the topics in the interaction among ethics, AI, and health disparities. The analysis framework and tool will be publicly disseminated and will have extensibility and integrability with the public portal analytics of "All of Us."