JMIR AI 2023;2:e52888

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

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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 socio-humanitarian issues and complexities. From these limitations emerges an imperative to strengthen AI/ML literacy in underserved communities and build a more diverse AI/ML design and development workforce engaged in health research.

Objective:
AI/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 to put ethics and fairness at the forefront of AI/ML applications to build equity in biomedical research, education, and healthcare.

Methods:
The AIM-AHEAD EEWG was created in 2021 with three co-chairs and 51 members in year 1 and two co-chairs and approximately 40 members in year 2. Members in both years included AIM-AHEAD principal investigators, co-investigators, leadership fellows, and research fellows. The EEWG used a modified Delphi approach using polling, ranking, and other exercises to facilitate discussions around tangible steps and key terms/definitions needed to ensure that ethics and fairness are at the front and center of AI/ML applications to build equity in biomedical research, education, and healthcare.

Results:
The EEWG developed a set of ethics and equity principles, a glossary, and an interview guide. The ethics and equity principles comprise of five (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/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/or predict potential limitations in their use(s) in AI/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 under-resourced institutions and communities where they are and where they are capable to engage and compete, there is higher potential to achieve needed diversity, ethics, and equity in AI/ML implementation in health research.


Article

JMIR Publications

JMIR

2

e52888

Please cite as: Hendricks-Sturrup R, Simmons M, Anders S, Aneni K, Wright Clayton E, Coco J, Collins B, Heitman E, Hussain S, Joshi K, Lemieux J, Lovett Novak L, Rubin D, Shanker A, Washington T, Waters G, Webb Harris J, Yin R, Wagner T, Yin Z, Malin B Developing Ethics and Equity Principles, Terms, and Engagement Tools to Advance Health Equity and Researcher Diversity in AI and Machine Learning: Modified Delphi Approach JMIR AI 2023;2:e52888 URL: https://ai.jmir.org/2023/1/e52888 DOI: 10.2196/52888

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