Strategies to Reduce Implicit Bias in Predictive Analytics

This white paper discusses the implications of implicit bias in predictive analytics, particularly within healthcare applications. It outlines the potential for bias to affect artificial intelligence and predictive tools, which may inadvertently perpetuate health inequities. The document emphasizes the need for an intentional approach to the development of these tools, focusing on bias mitigation and health equity. It describes the tendency of implicit bias to influence decision-making processes, particularly regarding the identification of patient needs based on historical data patterns. The paper presents actionable steps for data scientists, including the definition of affected populations, the use of diverse data sets, and the critical examination of algorithm outputs. Through these strategies, healthcare organizations can work towards improving the equity of predictive outputs while optimizing their operational goals. The target audience includes practitioners and developers in data science, as well as clinicians seeking to understand the ethical aspects of predictive tool usage.