A vast body of knowledge about community engagement praxis exists. Guidelines and frameworks are updated and operationalised by practitioners from many disciplines including community cultural development, community arts, social work, social sciences, architecture, and public health. However, this vital element is largely neglected in the AI ecosystem although many AI projects would benefit from considered attention to community engagement. For instance, in the health sector, AI and advanced analytics implementation in primary care should be a collaborative effort that involves patients and communities from diverse social, cultural, and economic backgrounds in an intentional and meaningful manner.
A Community Engagement Manager role could be introduced who would work with impacted communities throughout the AI-LC and for a fixed period post-deployment. Reciprocal and respectful relationships with impacted communities should be nurtured, and community expectations about both the engagement and the AI system should be defined and attended to. If impacted communities contain diverse language, ethnic, and cultural cohorts a Community Engagement Team from minority groups would be more appropriate. One role would be to develop tailored critical AI literacy programs for example. Organisations must put “the voices and experiences of those most marginalized at the centre” when implementing community engagement outcomes in an AI project.