Developing Ethics and Equity Principles, Terms, and Engagement Tools to Advance Health Equity and Researcher Diversity in AI and Machine Learning: Modified Delphi Approach.

Autor:	Hendricks-Sturrup R; National Alliance Against Disparities in Patient Health, Woodbridge, VA, United States., Simmons M; National Alliance Against Disparities in Patient Health, Woodbridge, VA, United States., Anders S; Vanderbilt University Medical Center, Nashville, TN, United States., Aneni K; Yale University, New Haven, CT, United States., Wright Clayton E; Vanderbilt University Medical Center, Nashville, TN, United States., Coco J; Vanderbilt University Medical Center, Nashville, TN, United States., Collins B; Vanderbilt University Medical Center, Nashville, TN, United States., Heitman E; University of Texas Southwestern Medical Center, Dallas, TX, United States., Hussain S; Fisk University, Nashville, TN, United States., Joshi K; University of Maryland, Baltimore County, Baltimore, MD, United States., Lemieux J; OCHIN, Portland, OR, United States., Lovett Novak L; Vanderbilt University Medical Center, Nashville, TN, United States., Rubin DJ; Temple University, Philadelphia, PA, United States., Shanker A; Meharry Medical College, Nashville, TN, United States., Washington T; AUC Data Science Initiative, Clark Atlanta University, Atlanta, GA, United States., Waters G; Morgan State University, Center for Equitable AI & Machine Learning Systems, Baltimore, MD, United States., Webb Harris J; Vanderbilt University Medical Center, Nashville, TN, United States., Yin R; University of Florida, Gainesville, FL, United States., Wagner T; University of North Texas Health Science Center, SaferCare Texas, Fort Worth, TX, United States., Yin Z; Vanderbilt University Medical Center, Nashville, TN, United States., Malin B; Vanderbilt University Medical Center, Nashville, TN, United States.
Jazyk:	angličtina
Zdroj:	JMIR AI [JMIR AI] 2023 Dec 06; Vol. 2, pp. e52888. Date of Electronic Publication: 2023 Dec 06.
DOI:	10.2196/52888
Abstrakt:	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. (©Rachele Hendricks-Sturrup, Malaika Simmons, Shilo Anders, Kammarauche Aneni, Ellen Wright Clayton, Joseph Coco, Benjamin Collins, Elizabeth Heitman, Sajid Hussain, Karuna Joshi, Josh Lemieux, Laurie Lovett Novak, Daniel J Rubin, Anil Shanker, Talitha Washington, Gabriella Waters, Joyce Webb Harris, Rui Yin, Teresa Wagner, Zhijun Yin, Bradley Malin. Originally published in JMIR AI (https://ai.jmir.org), 06.12.2023.)
Databáze:	MEDLINE
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