Table 1. Key topics that emerged from ACBC, across sessions, for each guiding question.

Guiding Question	Key Topics from Participant Responses
(1.1) Tell us about your barriers to using AI/ML	• Non-representative data sets • Biased algorithms • Issues with data access, privacy, & security • Lack of infrastructure • Complicated and inconsistent AI/ML language/terminology
(1.2 & 1.3) Tell us about ways to overcome some of these barriers & the resources you might need to address these barriers	• Community outreach and engagement • Meaningful collaboration with stakeholders • Equitable investments in infrastructure, training, and research
(2) Tell us about your thoughts on the importance of using AI/ML to promote health equity	• AI/ML can have positive implications for improving health disparities • Opportunities for AI/ML to predict diagnoses, knowledge, and patterns in health • AI/ML needs to be transparent, translatable, & relatable • Community-oriented AI/ML is essential to developing solutions
(3.1) Tell us about concerns you have in using AI/ML in special or unique populations	• Lack of transparency • Historical and cultural context • Need community buy-in from each different population AIM-AHEAD aims to engage
(3.2 & 3.3) Tell us how we can begin to address these concerns and/or biases & ways to engage these populations in AI/ML use and put the community first	• Connect with existing networks to communicate information about AI/ML to their communities • Build long-term relationships
(4. 1) Tell us about the best strategies for addressing diversity and inclusion in the field of AI/ML	• Encourage interest upstream through promotion and training • Recruitment of diverse individuals • Focus on retention
(4.2) Tell us what practicing cultural humility should look like when doing this work	• Culturally relevant & responsive engagement • Representation matters
(5.1) Tell us about encouraging collaboration among all types of stakeholder groups and promoting multi-disciplinary AI/ML to promote health equity	• Communicate mutual benefits • Build tools together • Create a dedicated space to foster collaboration
(5.2) Tell us about suggestions for building trust	• Address data privacy & security • Tell AI/ML success stories • Ethical AI/ML will lead to increased trust
(6) Tell us what success for AIM-AHEAD would look like to you	• AI/ML best practices & standards • Researcher/workforce diversity • Demonstrate results of AIM-AHEAD initiatives • AI/ML becomes a household word • Funding to under-resourced institutions/organizations
(7) Tell us about ensuring sustainability to help support communities, organizations, and institutions in the long term	• Measurable results are important • Communicating AI/ML real-life examples in everyday life help provide a frame of reference • Continuous stakeholder involvement and empowerment • Utilize existing networks & infrastructure
(8) Tell us how you would want to participate and engage with the AIM-AHEAD consortium	• Co-develop curriculum and lead AI/ML training • Regular communication & outreach from AIM-AHEAD • Develop best-practices & guidelines for AI/ML development and use