Applying a community-engaged participatory machine learning model.

Abstract

Although predictive algorithms have been described as the definitive solution to bias in health care, machine learning techniques may also propagate existing health inequities within the community context. However, there may be ways in which machine learning techniques can help community psychologists, public health researchers and practitioners identify patterns in data in a way that empowers improved outcomes. Incorporating community insight in all stages of machine learning research mitigates bias by positioning members of underrepresented communities as the experts of their lived experiences. As community psychologists already prioritize community-based participatory practices, we propose three core guiding principles for a community-engaged participatory model for research using machine learning techniques: shared decision-making, reflexivity and structural humility, and flexibility and adaptability. Guided by these three principles, we emphasize grounding priority setting, problem formation, model assumptions, and interpretation of the resulting algorithmic patterns in the truths born from the lived experiences of people closest to the problem. We also suggest opportunities for bidirectional and mutually empowering partnerships between algorithmic scientists and the communities to which their algorithms will be applied. Inclusion of community stakeholders in all stages of machine learning for health research provides an opportunity to develop algorithms that are both highly effective and ethically grounded in the lived experiences of target populations.