Predicting Opioid Use Outcomes in Minoritized Communities

Abstract

Within the healthcare space, machine learning algorithms can sometimes exacerbate racial, ethnic, and gender disparities, among others. Many machine learning algorithms are trained on data from majority populations, thereby generating less accurate or reliable results for minoritized groups [3]. For example, in a widely used algorithm, at a given risk score, the technique falsely concludes that Black individuals are healthier than equally sick White individuals [6]. Thus, such large-scale algorithms can often perpetuate biases. There has been limited work at exploring potential biases in algorithms deployed within minoritized communities. In particular, minimal research has detailed how biases may manifest in algorithms developed by insurance companies to predict opioid use outcomes, or opioid overdoses among people who use opioids in urban areas. An algorithm trained on data from white individuals may provide incorrect estimates for Hispanic/Latino individuals, perhaps resulting in adverse health outcomes.

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