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Developing Effective Methods for Addressing Contextual Bias in Forensic Science

NCJ Number
Date Published
January 2018
12 pages
This is the final summary overview of a research project that examined ways to address "contextual bias" in forensic science, taking into account the practical difficulties of implementing "blinding" or "masking" procedures, as well as ensuring that forensic examiners have access to the information they need to perform a rigorous scientific examination, while shielding them from exposure to contextual information that is unnecessary and with the potential to bias the forensic examination.

The project focused on three general approaches for managing contextual information. One is the "case manager model," which separates functions in the laboratory between case managers and examiners, so managers can be fully informed about context, but forensic examiners are provided with only the information needed for the analytical tasks they are trained to perform. A second means of preventing contextual bias is "sequential unmasking" or "linear sequential unmasking." This procedure sequences the order of various forensic analytical tasks in order to ensure that forensic examiners make certain key analytical judgments before being exposed to potentially biasing information. The third method assessed for preventing contextual bias is "blind re-examination," in which key judgments of an initial non-blind examiner are replicated by a second examiner who has not been exposed to potentially biasing information. The research addressed the use of the three models in bloodstain pattern analysis and handwriting analysis. The ultimate goal of the research was the development of practical protocols for managing contextual information that could be implemented by bloodstain pattern analysts and handwriting analysts. This report indicates that the research findings it discusses will have a continuing role in future discussions of contextual bias in forensic science and will be a significant factor in decisions about how best to deal with the problem of contextual bias. The project's methodology is described. 15 references

Date Published: January 1, 2018