A Probabilistic Formalisation of Contextual Bias: from Forensic Analysis to Systemic Bias in the Criminal Justice System

Abstract

Researchers have found evidence of contextual bias in forensic science, but the discussion of contextual bias is currently qualitative. We formalise existing empirical research and show 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 analyse results from empirical studies using this framework and employ 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 can lead to large, compounded biases in the final determination of guilt in a criminal trial.

The most consistent finding in forensic science is inconsistency

“…Second, it is also important to consider whether or not the inconsistencies arise from bias. Bias is a predictable and systematic variation (for a probabilistic formalization see [23]), whereas random variations can be regarded as 'noise' [24]. Inconsistencies due to bias (e.g., [4,5,9]), arise, for example, when examiners analyze the same evidence, but each time within different task-irrelevant contextual information (e.g., one examiner is told that the suspect confessed to the crime, whereas the other examiner is told that someone else confessed to the crime-see [22] for eight different sources of bias).…”