PDF(Pubmed)
Abstract：
BACKGROUND: Artificial intelligence (AI) and machine learning (ML) technology design and development continues to be rapid, despite major limitations in its current form as a practice and discipline to address all sociohumanitarian issues and complexities. From these limitations emerges an imperative to strengthen AI and ML literacy in underserved communities and build a more diverse AI and ML design and development workforce engaged in health research.
OBJECTIVE: AI and ML has the potential to account for and assess a variety of factors that contribute to health and disease and to improve prevention, diagnosis, and therapy. Here, we describe recent activities within the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Ethics and Equity Workgroup (EEWG) that led to the development of deliverables that will help put ethics and fairness at the forefront of AI and ML applications to build equity in biomedical research, education, and health care.
METHODS: The AIM-AHEAD EEWG was created in 2021 with 3 cochairs and 51 members in year 1 and 2 cochairs and ~40 members in year 2. Members in both years included AIM-AHEAD principal investigators, coinvestigators, leadership fellows, and research fellows. The EEWG used a modified Delphi approach using polling, ranking, and other exercises to facilitate discussions around tangible steps, key terms, and definitions needed to ensure that ethics and fairness are at the forefront of AI and ML applications to build equity in biomedical research, education, and health care.
RESULTS: The EEWG developed a set of ethics and equity principles, a glossary, and an interview guide. The ethics and equity principles comprise 5 core principles, each with subparts, which articulate best practices for working with stakeholders from historically and presently underrepresented communities. The glossary contains 12 terms and definitions, with particular emphasis on optimal development, refinement, and implementation of AI and ML in health equity research. To accompany the glossary, the EEWG developed a concept relationship diagram that describes the logical flow of and relationship between the definitional concepts. Lastly, the interview guide provides questions that can be used or adapted to garner stakeholder and community perspectives on the principles and glossary.
CONCLUSIONS: Ongoing engagement is needed around our principles and glossary to identify and predict potential limitations in their uses in AI and ML research settings, especially for institutions with limited resources. This requires time, careful consideration, and honest discussions around what classifies an engagement incentive as meaningful to support and sustain their full engagement. By slowing down to meet historically and presently underresourced institutions and communities where they are and where they are capable of engaging and competing, there is higher potential to achieve needed diversity, ethics, and equity in AI and ML implementation in health research.
OBJECTIVE: AI and ML has the potential to account for and assess a variety of factors that contribute to health and disease and to improve prevention, diagnosis, and therapy. Here, we describe recent activities within the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Ethics and Equity Workgroup (EEWG) that led to the development of deliverables that will help put ethics and fairness at the forefront of AI and ML applications to build equity in biomedical research, education, and health care.
METHODS: The AIM-AHEAD EEWG was created in 2021 with 3 cochairs and 51 members in year 1 and 2 cochairs and ~40 members in year 2. Members in both years included AIM-AHEAD principal investigators, coinvestigators, leadership fellows, and research fellows. The EEWG used a modified Delphi approach using polling, ranking, and other exercises to facilitate discussions around tangible steps, key terms, and definitions needed to ensure that ethics and fairness are at the forefront of AI and ML applications to build equity in biomedical research, education, and health care.
RESULTS: The EEWG developed a set of ethics and equity principles, a glossary, and an interview guide. The ethics and equity principles comprise 5 core principles, each with subparts, which articulate best practices for working with stakeholders from historically and presently underrepresented communities. The glossary contains 12 terms and definitions, with particular emphasis on optimal development, refinement, and implementation of AI and ML in health equity research. To accompany the glossary, the EEWG developed a concept relationship diagram that describes the logical flow of and relationship between the definitional concepts. Lastly, the interview guide provides questions that can be used or adapted to garner stakeholder and community perspectives on the principles and glossary.
CONCLUSIONS: Ongoing engagement is needed around our principles and glossary to identify and predict potential limitations in their uses in AI and ML research settings, especially for institutions with limited resources. This requires time, careful consideration, and honest discussions around what classifies an engagement incentive as meaningful to support and sustain their full engagement. By slowing down to meet historically and presently underresourced institutions and communities where they are and where they are capable of engaging and competing, there is higher potential to achieve needed diversity, ethics, and equity in AI and ML implementation in health research.
摘要：
背景：人工智能（AI）和机器学习（ML）技术的设计和开发持续迅速，尽管在目前的形式作为解决所有社会人道主义问题和复杂性的实践和纪律存在重大限制。从这些限制中，迫切需要在服务不足的社区中加强AI和ML素养，并建立更多样化的AI和ML设计和开发劳动力，从事健康研究。
目的：AI和ML有可能解释和评估导致健康和疾病的各种因素，并改善预防，诊断,和治疗。这里,我们描述了人工智能/机器学习联盟内部最近的活动，以促进健康公平和研究人员多样性（AIM-AHEAD）道德和公平工作组（EEWG），这些活动导致了可交付成果的开发，这将有助于将道德和公平置于AI和ML应用的最前沿，以建立生物医学研究的公平性。教育,和医疗保健。
方法：AIM-AHEADEEWG创建于2021年，第1年有3个联合主席和51个成员，第2年有约40个成员。这两年的成员包括AIM-AHEAD主要调查员，协研究者，领导研究员，和研究员。EEWG使用了一种使用轮询的改进的Delphi方法，排名,和其他活动，以促进围绕切实步骤的讨论，关键术语,和定义需要确保道德和公平处于AI和ML应用的最前沿，以建立生物医学研究的公平性，教育,和医疗保健。
结果：EEWG制定了一套道德和公平原则，词汇表，和采访指南。道德和公平原则包括5个核心原则，每个都有子部分，阐明了与历史上和目前代表性不足的社区的利益相关者合作的最佳做法。词汇表包含12个术语和定义，特别强调最佳发展，精致,以及AI和ML在健康公平研究中的实施。为了配合词汇表，EEWG开发了一个概念关系图，描述了定义概念的逻辑流程和定义概念之间的关系。最后,面试指南提供了可以使用或调整的问题，以获得利益相关者和社区对原则和词汇表的观点。
结论：需要围绕我们的原则和术语表持续参与，以识别和预测它们在AI和ML研究环境中使用的潜在局限性。特别是对于资源有限的机构。这需要时间,仔细考虑,和诚实的讨论，围绕什么将参与激励分类为有意义的，以支持和维持他们的全面参与。通过放慢速度，以满足历史上和目前资源不足的机构和社区，以及它们能够参与和竞争的地方，实现所需多样性的潜力更大，伦理,以及健康研究中AI和ML实施的公平性。
目的：AI和ML有可能解释和评估导致健康和疾病的各种因素，并改善预防，诊断,和治疗。这里,我们描述了人工智能/机器学习联盟内部最近的活动，以促进健康公平和研究人员多样性（AIM-AHEAD）道德和公平工作组（EEWG），这些活动导致了可交付成果的开发，这将有助于将道德和公平置于AI和ML应用的最前沿，以建立生物医学研究的公平性。教育,和医疗保健。
方法：AIM-AHEADEEWG创建于2021年，第1年有3个联合主席和51个成员，第2年有约40个成员。这两年的成员包括AIM-AHEAD主要调查员，协研究者，领导研究员，和研究员。EEWG使用了一种使用轮询的改进的Delphi方法，排名,和其他活动，以促进围绕切实步骤的讨论，关键术语,和定义需要确保道德和公平处于AI和ML应用的最前沿，以建立生物医学研究的公平性，教育,和医疗保健。
结果：EEWG制定了一套道德和公平原则，词汇表，和采访指南。道德和公平原则包括5个核心原则，每个都有子部分，阐明了与历史上和目前代表性不足的社区的利益相关者合作的最佳做法。词汇表包含12个术语和定义，特别强调最佳发展，精致,以及AI和ML在健康公平研究中的实施。为了配合词汇表，EEWG开发了一个概念关系图，描述了定义概念的逻辑流程和定义概念之间的关系。最后,面试指南提供了可以使用或调整的问题，以获得利益相关者和社区对原则和词汇表的观点。
结论：需要围绕我们的原则和术语表持续参与，以识别和预测它们在AI和ML研究环境中使用的潜在局限性。特别是对于资源有限的机构。这需要时间,仔细考虑,和诚实的讨论，围绕什么将参与激励分类为有意义的，以支持和维持他们的全面参与。通过放慢速度，以满足历史上和目前资源不足的机构和社区，以及它们能够参与和竞争的地方，实现所需多样性的潜力更大，伦理,以及健康研究中AI和ML实施的公平性。
