A probabilistic formalization of contextual bias in forensic analysis: Evidence that examiner bias leads to systemic bias in the criminal justice system

TL;DR

This paper introduces a probabilistic framework to quantify how small biases in forensic analysis can systematically influence legal outcomes, demonstrating that minor initial biases can significantly skew guilt determinations.

Contribution

It formalizes empirical research on forensic bias using Bayesian methods and extends understanding of how biases propagate through the legal system.

Findings

Minor biases in forensic analysis can lead to large systemic biases in guilt determination.

The probabilistic framework quantifies bias propagation throughout the legal process.

Simulations show bias amplification in the absence of corrective measures.

Abstract

Although researchers have found evidence contextual bias in forensic science, the discussion of contextual bias is currently qualitative. We formalize years of empirical research and extend this research by showing quantitatively how biases can be propagated throughout the legal system, all the way up to the final determination of guilt in a criminal trial. We provide a probabilistic framework for describing how information is updated in a forensic analysis setting by using the ratio form of Bayes' rule. We analyze results from empirical studies using our framework and use simulations to demonstrate how bias can be compounded where experiments do not exist. We find that even minor biases in the earlier stages of forensic analysis lead to large, compounded biases in the final determination of guilt in a criminal trial.