Advancing Community Engaged Approaches to Identifying Structural Drivers of Racial Bias in Health Diagnostic Algorithms

TL;DR

This paper explores how societal and community factors influence racial bias in health diagnostic algorithms, emphasizing the importance of understanding social context and community trauma in addressing disparities.

Contribution

It introduces a system dynamics modeling approach to analyze societal drivers of racial bias in healthcare algorithms, highlighting the need to consider community experiences and societal context.

Findings

Community trauma influences healthcare-seeking behavior.

Disparate initial conditions affect algorithm data quality.

Focusing solely on algorithms is insufficient for reducing disparities.

Abstract

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.