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 AI2023Vol. 2, pp. e52888–e52888
Citations Over TimeTop 17% of 2023 papers
Rachele Hendricks‐Sturrup, Malaika Simmons, Shilo Anders, Kammarauche Aneni, Ellen Wright Clayton, Joseph Coco, Benjamin Collins, Elizabeth Heitman, Sajid Hussain, Karuna Pande Joshi, Josh Lemieux, Laurie L. Novak, Daniel J. Rubin, Anil Shanker, Talitha Washington, G Waters, Joyce Harris, Rui Yin, Teresa Wagner, Zhijun Yin, Bradley Malin
Abstract
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.
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