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Telling us less than what they know: Expert inconclusive reports conceal exculpatory evidence in forensic cartridge-case comparisons
Abstract
Cartridge-case comparison experts are tasked with determining whether a reference sample associated with the suspect originated from the same source as the evidence sample found at the crime scene. Same-source reports have strong inculpatory value and different-source reports have strong exculpatory value. Surprisingly, inconclusive reports have strong exculpatory value as they occur much more frequently for actual nonmatches than for actual matches. We used a signal detection model to test our hypothesis that some experts report inconclusive when they detect a nonmatch. Consistent with this hypothesis, we found that even when examiners were able to perfectly discriminate matches from nonmatches, they rendered inconclusive reports on 32% of nonmatch trials. Experts are biased to avoid rendering different-source reports, which conceals exculpatory information that innocent persons desperately need to establish their innocence. We argue that this biased examiner might result from an adversarial allegiance bias combined with a flawed response scale. (PsycInfo Database Record (c) 2025 APA, all rights reserved)
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“For example, signal detection analysis shows that forensic science experts in firearms evidence deliberately do not report elimination conclusions (Smith & Wells, 2023).”
From Biased and biasing: the hidden bias cascade and bias snowball effects (2025) · cites this paper as Smith & Wells, 2023
In the citing paper
“This explicit intentional bias of not reporting exculpatory evidence has serious implications for the justice and legal systems because it deprives innocent suspects of the evidence they need to prove their innocence (Smith & Wells, 2023).”
From Biased and biasing: the hidden bias cascade and bias snowball effects (2025) · cites this paper as Smith & Wells, 2023
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Biased and biasing: the hidden bias cascade and bias snowball effects
Dror, I. E. (2025) · Behavioral Sciences · doi:10.3390/bs15040490 · Cited by 29
Gutierrez, R. E., & Prokesch, E. J. (2024) · Journal of Forensic Sciences · doi:10.1111/1556-4029.15531 · Cited by 9
More unjustified inferences from limited data in
Gutierrez, R. E. (2024) · Law Probability and Risk · doi:10.1093/lpr/mgae014 · Cited by 2
Gutierrez, R. E., & Prokesch, E. J. (2024) · Journal of Forensic Sciences · doi:10.1111/1556-4029.15613 · Cited by 1
On the Importance of Recognition and Mitigation of Bias in Forensic Science
Davis, D., Miller, G. A., Hart, D. J., & Hogan, A. A. (2024) · Springer eBooks · doi:10.1007/978-3-031-56556-4_5 · Cited by 2
Gutierrez, R. E., & Prokesch, E. J. (2024) · Journal of Forensic Sciences · doi:10.1111/1556-4029.15660
Monson, K. L., Smith, E. D., & Peters, E. M. (2025) · Journal of Forensic Sciences · doi:10.1111/1556-4029.70004
Prokesch, E. J., & Gutierrez, R. E. (2025) · Forensic Science International · doi:10.1016/j.forsciint.2025.112590
A rating-only approach to perceptual discrimination
Kucina, T., Kohl, A. T., Sauer, J. D., & Palmer, M. A. (2025) · Royal Society Open Science · doi:10.1098/rsos.241032
Metadata
- Authors
- Smith, A. M., & Wells, G. L.
- Venue
- Journal of Applied Research in Memory and Cognition
- Published
- 2024-03
- Volume
- 13
- Issue
- 1
- Pages
- 147-152
- ISSN
- 2211-3681 (ISSN-L)
- 2211-369X
- OpenAlex
- W4387265709
- Cited by
- 9
- References
- 0
- Citations by year
- 2025: 4 · 2024: 5
- FWCI
- 2.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Applied Research in Memory and Cognition (publishedVersion)
- Iowa State University Digital Repository (Iowa State University) (submittedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Deception detection and forensic psychology
- Social Sciences › Law › Legal processes and jurisprudence
- Social Sciences › Law › Jury Decision Making Processes
- Keywords
- exculpatory evidence, source identification, evaluator bias, response scales, signal detection model, forensic evidence reliability
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