Abstract for: Advancing Community Engaged Approaches to Identifying Structural Drivers of Racial Bias in Health Diagnostic Algorithms
Much concern has been raised about bias and the use of machine learning algorithms in healthcare, especially as it relates to perpetuating racial discrimination and health disparities. While proposed solutions frequently focus on increasing data collection for algorithm improvement, the wider context of structural inequity that frames the data generating process may be equally important to consider. Following a system dynamics workshop at the Data for Black Lives II conference, a group of conference participants interested in building capabilities to use system dynamics to understand complex social issues convened monthly to use SD to explore issues related to racial bias in AI and health disparities. We present results and insights from this community-based system dynamics modeling process and highlight the importance of centering the discussion of data and healthcare on people and their experiences with healthcare and science, and recognizing the societal context in which the algorithm is operating. Collective memory of community trauma, through deaths attributed to poor healthcare, and negative experiences with healthcare are endogenous drivers of seeking treatment and experiencing effective care, which impact the availability of training data for algorithms. These drivers have drastically disparate initial conditions for different racial groups and point to the limited impact of focusing solely on improving diagnostic algorithms for achieving better health outcomes for some groups.