A Forensic Science-Based Model for Identifying and Mitigating Forensic Mental Health Expert Biases.

Abstract

In 2020, cognitive neuroscientist Itiel Dror developed a cognitive framework to address biases influenced by cognitive processes and external pressures in decisions made by forensic experts. Dror's model highlights how ostensibly objective data, such as toxicology or fingerprints, can be affected by bias driven by contextual, motivational, and organizational factors. Forensic mental health evaluations, often more subjective than physical forensic evidence analysis, are particularly vulnerable to these cognitive biases. Dror identified six expert fallacies, such as the belief that bias only affects unethical or incompetent practitioners, and proposed a pyramidal model showing how biases infiltrate expert decisions. This article adapts Dror's model to forensic mental health, exploring how biases influence data collection and interpretation and proposing mitigation strategies like Linear Sequential Unmasking-Expanded (LSU-E). We emphasize that mitigating cognitive biases requires structured, external strategies, as self-awareness alone is insufficient. By applying Dror's concepts and framework, we offer a practical approach to reduce biases and improve the fairness and accuracy of forensic mental health assessments.