Community perspectives on AI/ML and health equity: AIM-AHEAD nationwide stakeholder listening sessions

Artificial intelligence and machine learning (AI/ML) tools have the potential to improve health equity. However, many historically underrepresented communities have not been engaged in AI/ML training, research, and infrastructure development. Therefore, AIM-AHEAD (Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity) seeks to increase participation and engagement of researchers and communities through mutually beneficial partnerships. The purpose of this paper is to summarize feedback from listening sessions conducted by the AIM-AHEAD Coordinating Center in February 2022, titled the “AIM-AHEAD Community Building Convention (ACBC).” A total of six listening sessions were held over three days. A total of 977 people registered with AIM-AHEAD to attend ACBC and 557 individuals attended the listening sessions across stakeholder groups. Facilitators led the conversation based on a series of guiding questions, and responses were captured through voice and chat via the Slido platform. A professional third-party provider transcribed the audio. Qualitative analysis included data from transcripts and chat logs. Thematic analysis was then used to identify common and unique themes across all transcripts. Six main themes arose from the sessions. Attendees felt that storytelling would be a powerful tool in communicating the impact of AI/ML in promoting health equity, trust building is vital and can be fostered through existing trusted relationships, and diverse communities should be involved every step of the way. Attendees shared a wealth of information that will guide AIM-AHEAD’s future activities. The sessions highlighted the need for researchers to translate AI/ML concepts into vignettes that are digestible to the larger public, the importance of diversity, and how open-science platforms can be used to encourage multi-disciplinary collaboration. While the sessions confirmed some of the existing barriers in applying AI/ML for health equity, they also offered new insights that were captured in the six themes.Author summary: Artificial intelligence and machine learning (AI/ML) have gained significant traction in the field of healthcare over the past several years. Innovative clinical applications are at the forefront of this “next frontier;” however, AI/ML can also be used to push the envelope for health equity by providing these tools to stakeholders at the grassroots level. We held nation-wide listening sessions with stakeholders from diverse organizations and institutions who are interested in using AI/ML to address health disparities, in order to shed light on the unique barriers these groups face. We were able to gather insight on how AI/ML tools could be improved, opportunities to increase diversity in data science, and using trusted community networks to ensure sustainability. This manuscript offers an approach to improving AI/ML data, infrastructure, and training that places community voices at the forefront in order to empower stakeholders to effect change.