Ethics Core (FABRIC)

道德核心 (FABRIC)

• 批准号: 10473062
• 负责人: Bradley A. Malin
• 金额: $ 54.3万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-08 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10473062
• 关键词: Address; Algorithms; Area; Artificial Intelligence; Behavioral Research; Benefits and Risks; Bioethics; Biomedical Research; Bridge to Artificial Intelligence; Catalogs; Climacteric; Clinical; Collaborations; Collection; Communities; Consent; Data; Data Collection; Data Protection; Data Set; Development; Economics; Ensure; Environment; Ethical Issues; Ethics; European; Foundations; Future; Generations; Guidelines; Health Care Research; Healthcare; Human; Individual; Informatics; Infrastructure; Knowledge; Laws; Learning; Literature; Machine Learning; Medicine; Persons; Privacy; Process; Public Policy; Public Relations; Reflex action; 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; digital health; distrust; experience; health care delivery; human centered design; implementation science; improved; interdisciplinary approach; legal implication; multidisciplinary; outreach; prevent; 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.

Bridge 2AI：一个公平的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， 改善医疗保健和生物医学研究的机会。然而，AI/ML也提出了一些重要的问题， 涉及道德和信任，包括定义同意和重新使用个人数据的参数，保护 隐私，确保透明度和利益相关者参与这项研究，并制定和 部署对所有人都有用的工具。如果没有一套道德上健全的原则和实践 在广泛的生物医学环境中可推广和可重用，AI/ML可能会违反个人 这一做法扩大了公平与平等之间的差距，并煽动了不信任的火焰，最近发生的事件就是例证。 研究表明种族偏见如何影响临床决策算法。我们对FAIR AI Bridge Center的愿景 - 伦理核心（FABRIC-Ethics）旨在确保AI/ML以道德和可信的方式开发和应用。 方式FABRIC-Ethics将支持Bridge 2AI计划通过使其更具道德性来实现可持续发展 在四年的项目期结束时， 为了实现这一愿景，我们将使用一个迭代和反思的四步循环：1）支架，2）评估，3）促进 和4）评估和教育，或安全，提供一个平台，召集，分析和策划，公共 以多学科的方式进行关系和原创研究。我们将与Bridge 2AI计划合作， 制定AI/ML（ETAI）的道德和可信原则，以解决现有和未来的实践， 生物医学AI研究与应用。其中包括数据的收集和管理， 以及AI/ML技术和AI/ML应用的部署。与Bridge 2AI项目密切合作 及其数据生成项目（DGP），我们将进行一次封闭式和开放式调查，讨论优先事项， 和Bridge 2AI 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 将与多种机制并行运行，以建立一个学习型ETAI社区，召集Bridge 2AI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 Bridge 2AI中心和更广泛的社区。我们的核心将进一步开发数字健康清单 和框架，准备Bridge 2AI DGP评估：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Bridge 2AI DGP合作，分享有关ETAI的知识， 制定原则和最佳做法，并为ETAI的可持续发展召开会议 超越Bridge 2AI的文化为核心组建的团队在广泛的领域拥有专业知识，包括 生物伦理学，数字健康研究伦理学，法律，公共政策，AI/ML，数据保护，信息学，医学，人类- 以设计为中心，实施科学和社区参与。为了确保成功，FABRIC-Ethics将 由四名在多学科方法研究伦理问题方面有良好记录的PI领导， 技术、中心管理和核心支持。