Developing Ethics and Equity Principles, Terms, and Engagement Tools to Advance Health Equity and Researcher...

Area of Science:

• Biomedical research
• Health equity
• Artificial intelligence and machine learning

Background:

• Rapid advancement in AI/ML technology design and development.
• Identified limitations in addressing complex sociohumanitarian issues.
• Imperative to enhance AI/ML literacy in underserved communities and diversify the workforce in health research.

Purpose of the Study:

• Leverage AI/ML to assess health and disease factors and improve medical outcomes.
• Describe activities of the AIM-AHEAD Ethics and Equity Workgroup (EEWG).
• Develop deliverables to prioritize ethics and fairness in AI/ML for health equity.

Main Methods:

• Established the AIM-AHEAD EEWG in 2021 with diverse membership.
• Utilized a modified Delphi approach with polling and ranking exercises.
• Facilitated discussions on tangible steps, terms, and definitions for ethical AI/ML in health equity.

Main Results:

• Developed a set of 5 core ethics and equity principles with subparts.
• Created a glossary of 12 terms and definitions for AI/ML in health equity research.
• Produced an interview guide for stakeholder perspectives and a concept relationship diagram.

Conclusions:

• Emphasize the need for ongoing engagement with principles and glossary.
• Address potential limitations, especially for institutions with limited resources.
• Advocate for a deliberate approach to foster diversity, ethics, and equity in AI/ML for health research.