Ethics Core (FABRIC)

道德核心 (FABRIC)

• 批准号: 10662376
• 负责人: Bradley A. Malin
• 金额: $ 121.72万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10662376
• 关键词: Address; Algorithms; Area; Artificial Intelligence; Behavioral Research; Benefits and Risks; Big Data; Bioethics; Biomedical Research; Bridge to Artificial Intelligence; Catalogs; Climacteric; Clinical; Collaborations; Collection; Communities; Consent; Core Assembly; Data; Data Collection; Data Protection; Data Set; Development; Economics; Ensure; Environment; Equity; Ethical Issues; Ethics; European; Foundations; Future; Generations; Guidelines; Healthcare; Human; Individual; Informatics; Infrastructure; Knowledge; Laws; Learning; Literature; Machine Learning; Medicine; Needs Assessment; Persons; Privacy; Process; Public Policy; Public Relations; Regulation; Research; Research Ethics; Rights; Running; Societies; Surveys; Sustainable Development; Technology; Time; Trust; Unconscious State; Vision; Work; World Health Organization; adverse outcome; clinical decision-making; community engagement; data management; decision making algorithm; digital health; digital tool; distrust; experience; health care delivery; human centered design; implementation science; improved; interdisciplinary approach; learning community; legal implication; multidisciplinary; outreach; prevent; privacy protection; programs; racial bias; response; scaffold; social; success; symposium; technology development; tool; trustworthiness; usability; willingness

Bridge2AI: a FAIR AI BRIDGE Center (FABRIC) Ethics Core Summary The use of artificial intelligence (AI), and particularly machine learning (ML), in healthcare opens up many opportunities to improve healthcare and biomedical research. However, AI/ML also raise important issues that implicate ethics and trust, including defining parameters for consent and re-use of personal data, protecting privacy, ensuring transparency and engagement with stakeholders about this research, and developing and deploying tools that are useful and valid for all people. Without an ethically robust set of principles and practices that are generalizable and reusable in a wide range of biomedical environments, AI/ML could violate personal rights, widen the gap between fairness and equality, and fan the flames of mistrust, as exemplified by recent work showing how racial bias can influence clinical decision algorithms. Our vision for the FAIR AI Bridge Center - Ethics Core (FABRIC-Ethics) is to ensure that AI/ML is developed and applied in an ethical and trustworthy manner. FABRIC-Ethics will support the Bridge2AI program to become sustainable by making it more ethical and trustworthy by the end of the four-year project period. To realize this vision, we will use an iterative and reflective four-step cycle: 1) Scaffold, 2) Assess, 3) Facilitate and 4) Evaluate and educate, or SAFE, to provide a platform for convening, analyzing and curating, public relations and original research in a multidisciplinary manner. We will work with the Bridge2AI program to formulate ethical and trustworthy principles for AI/ML (ETAI) to address existing and future practices in biomedical AI research and applications. These include the collection and management of data, the development and deployment of AI/ML technologies and AI/ML applications. In close collaboration with the Bridge2AI program and its Data Generation Projects (DGPs), we will conduct a closed- and open-ended survey, discuss priorities and experiences with Bridge2AI DGPs, and develop an open, curated catalog of relevant literature. These efforts will run in parallel with multiple mechanisms for building a learning ETAI community, convening Bridge2AI data generation projects to distill best practices, and organizing studio sessions to support contact with the other core areas of the Bridge2AI Center and the broader community. Our core will further develop a digital health checklist and framework that prepares Bridge2AI DGPs to evaluate: 1) access and usability, 2) risks and benefits, 3) privacy and 4) data management. We will work with the Bridge2AI DGPs to share knowledge about ETAI, inform the development of principles and best practices, and to set up conferences for sustainable development of ETAI culture beyond Bridge2AI. The team assembled for the core has expertise in a wide range of areas, including bioethics, digital health research ethics, law, public policy, AI/ML, data protection, informatics, medicine, human- centered design, implementation science, and community engagement. To ensure success, FABRIC-Ethics will be led by four PIs with a proven track record in multidisciplinary approaches to the study of ethical issues in technology, center management, and core support.

Bridge2AI：公平 AI BRIDGE 中心 (FABRIC) 道德核心摘要 人工智能 (AI)，特别是机器学习 (ML) 在医疗保健领域的应用，为医疗保健行业带来了许多机遇 改善医疗保健和生物医学研究的机会。然而，人工智能/机器学习也提出了一些重要问题： 涉及道德和信任，包括定义同意和重复使用个人数据的参数，保护 隐私，确保透明度并与利益相关者参与这项研究，以及开发和 部署对所有人有用且有效的工具。没有一套道德上健全的原则和实践 由于人工智能/机器学习在广泛的生物医学环境中具有通用性和可重复使用性，因此可能会侵犯个人隐私 权利，扩大公平与平等之间的差距，煽动不信任的火焰，最近的例子就是如此 研究表明种族偏见如何影响临床决策算法。我们对 FAIR AI Bridge 中心的愿景 - 道德核心（FABRIC-Ethics）是确保人工智能/机器学习以道德和值得信赖的方式开发和应用 方式。 FABRIC-Ethics 将通过使其更加道德来支持 Bridge2AI 计划的可持续发展 并在四年项目期结束时值得信赖。 为了实现这一愿景，我们将使用迭代和反思的四步循环：1）支架，2）评估，3）促进 4) 评估和教育（SAFE），提供一个平台，用于召集、分析和策划、公开 以多学科的方式进行关系和原创性研究。我们将与 Bridge2AI 计划合作 制定人工智能/机器学习 (ETAI) 的道德和值得信赖的原则，以解决现有和未来的实践 生物医学人工智能研究与应用。其中包括数据的收集和管理、开发 以及人工智能/机器学习技术和人工智能/机器学习应用的部署。与 Bridge2AI 项目密切合作 及其数据生成项目 (DGP)，我们将进行封闭式和开放式调查，讨论优先事项 以及 Bridge2AI DGP 的经验，并开发一个开放的、精心策划的相关文献目录。这些努力 将与构建学习型ETAI社区的多种机制并行运行，召集Bridge2AI数据 生成项目以提炼最佳实践，并组织工作室会议以支持与其他核心的联系 Bridge2AI 中心和更广泛社区的领域。我们的核心将进一步开发数字健康检查表 以及准备 Bridge2AI DGP 评估的框架：1) 访问和可用性，2) 风险和收益，3) 隐私和 4) 数据管理。我们将与 Bridge2AI DGP 合作，分享有关 ETAI 的知识，告知 制定原则和最佳实践，并设立 ETAI 可持续发展会议 Bridge2AI 之外的文化。为核心组建的团队拥有广泛领域的专业知识，包括 生物伦理学、数字健康研究伦理学、法律、公共政策、人工智能/机器学习、数据保护、信息学、医学、人类 以设计、实施科学和社区参与为中心。为了确保成功，FABRIC-Ethics 将 由四位 PI 领导，他们在多学科方法研究伦理问题方面拥有良好的记录 技术、中心管理和核心支持。