Advancing Community Engaged Approaches to Identifying Structural Drivers
  of Racial Bias in Health Diagnostic Algorithms
Jill A. Kuhlberg Irene Headen Ellis A. Ballard Donald Martin Jr.
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.