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：一个公平的AI桥梁中心（面料）伦理核心总结 在医疗保健中使用人工智能（AI），尤其是机器学习（ML） 改善医疗保健和生物医学研究的机会。但是，AI/ML还提出了重要问题 暗示道德和信任，包括定义同意和重复使用个人数据的参数，保护 隐私，确保与利益相关者有关这项研究，发展和发展的透明度和参与 部署对所有人有用且有效的工具。没有道德上强大的原则和实践 在广泛的生物医学环境中，AI/ML可能会违反个人 权利，扩大公平与平等之间的鸿沟，并驱动不信任的火焰，例如 工作表明种族偏见如何影响临床决策算法。我们对公平AI桥中心的愿景 - 道德核心（织物 - 伦理学）是为了确保在道德和值得信赖的道德上开发和应用AI/ML 方式。织物伦理学将支持Bridge2AI计划，以使其更具道德感来实现可持续性 并在四年期项目结束时值得信赖。 要实现这一愿景，我们将使用迭代和反思性的四步周期：1）脚手架，2）评估，3）促进 4）评估，教育或安全，为召集，分析和策展提供一个平台 关系和原始研究以多学科的方式。我们将与Bridge2ai计划合作 为AI/ML（ETAI）制定道德和值得信赖的原则，以解决现有的和未来的实践 生物医学AI研究和应用。其中包括数据的收集和管理，开发 以及AI/ML技术和AI/ML应用程序的部署。与Bridge2ai计划密切合作 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，并开发了相关文献的开放，精心策划的目录。这些努力 将与建立学习ETAI社区的多种机制并行运行 发电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中心和更广泛的社区的地区。我们的核心将进一步开发数字健康清单 和准备Bridge2AI DGP进行评估的框架：1）访问和可用性，2）风险和福利，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，并为ETAI的可持续发展开设会议 Bridge2ai以外的文化。为核心组装的团队在各个领域都有专业知识，包括 生物伦理学，数字健康研究伦理，法律，公共政策，AI/ML，数据保护，信息学，医学，人类 - 集中的设计，实施科学和社区参与。为了确保成功，织物伦理学将 由四个PI领导，在多学科方法中具有良好的往绩来研究道德问题 技术，中心管理和核心支持。