Advancing Community Engaged Approaches to Identifying Structural Drivers of Racial Bias in Health Diagnostic Algorithms
This work addresses racial health disparities for marginalized communities by highlighting systemic issues, but it is incremental as it builds on existing concerns without introducing new technical methods.
The paper tackles racial bias in health diagnostic algorithms by using community-engaged system dynamics modeling to identify structural drivers like collective trauma and negative healthcare experiences, finding that these factors create disparate data conditions and limit the impact of algorithmic improvements alone.
Much attention and concern has been raised recently about bias and the use of machine learning algorithms in healthcare, especially as it relates to perpetuating racial discrimination and health disparities. Following an initial system dynamics workshop at the Data for Black Lives II conference hosted at MIT in January of 2019, a group of conference participants interested in building capabilities to use system dynamics to understand complex societal issues convened monthly to explore issues related to racial bias in AI and implications for health disparities through qualitative and simulation modeling. In this paper we present results and insights from the 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 where 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 and quality of data for algorithms. These drivers have drastically disparate initial conditions for different racial groups and point to limited impact of focusing solely on improving diagnostic algorithms for achieving better health outcomes for some groups.