The forensic confirmation bias: Problems, perspectives, and proposed solutions.
Abstract
As illustrated by the mistaken, high-profile fingerprint identification of Brandon Mayfield in the Madrid Bomber case, and consistent with a recent critique by the National Academy of Sciences (2009), it is clear that the forensic sciences are subject to contextual bias and fraught with error. In this article, we describe classic psychological research on primacy, expectancy effects, and observer effects, all of which indicate that context can taint people's perceptions, judgments, and behaviors. Then we describe recent studies indicating that confessions and other types of information can set into motion forensic confirmation biases that corrupt lay witness perceptions and memories as well as the judgments of experts in various domains of forensic science. Finally, we propose best practices that would reduce bias in the forensic laboratory as well as its influence in the courts. © 2013 Society for Applied Research in Memory and Cognition.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Psychologists Kassin, Dror, and Kukucka describe how an actor may be biased by outside information and how a forensic analyst's conclusions may bias others in the criminal justice system.
“For example, psychologists Saul Kassin, Itiel Dror, and Jeff Kukucka describe not only how an actor may be biased by outside information and other factors but also how the conclusions reached by, say, a forensic analyst may bias others in the criminal justice system (Kassin et al. 2013).”
From Wrongful Convictions (2020) · cites this paper as Kassin et al. 2013
False confessions can override evidence, rewrite witnesses' memories, corrupt eyewitness identifications, and cause forensic scientists to erroneously identify a suspect.
“There are many other examples of this throughout the justice and legal systems; some examples are how false confessions will override evidence, rewrite witnesses’ memories, and corrupt eyewitness identifications, as well as how they can cause forensic scientists to erroneously identify a suspect (Hasel & Kassin, 2009; Kassin et al., 2013; see also the cases of the Central Park Five or Brendan Das”
From Biased and biasing: the hidden bias cascade and bias snowball effects (2025) · cites this paper as Kassin et al., 2013
Cites
Tversky, A., & Kahneman, D. (1974) · Science · doi:10.1126/science.185.4157.1124 · Cited by 28,810 · see citing works
The case for motivated reasoning.
Kunda, Z. (1990) · Psychological Bulletin · doi:10.1037/0033-2909.108.3.480 · Cited by 9,022 · see citing works
Confirmation, disconfirmation, and information in hypothesis testing
Klayman, J., & Ha, Y.-W. (1987) · Psychological Review · doi:10.1037/0033-295x.94.2.211 · Cited by 2,094 · see citing works
Strengthening Forensic Science in the United States: A Path Forward
Council, N. R. (2009) · CrimRxiv · doi:10.21428/cb6ab371.b2d683d2 · Cited by 1,461 · see citing works
The effect of experimenter bias on the performance of the albino rat
Rosenthal, R., & Fode, K. L. (2007) · Behavioral Science · doi:10.1002/bs.3830080302 · Cited by 384 · see citing works
Accuracy and reliability of forensic latent fingerprint decisions
Ulery, B. T., Hicklin, R. A., Buscaglia, J., & Roberts, M. A. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1018707108 · Cited by 350 · see citing works
Evidence of Motivational Influences in Early Visual Perception: Hunger Modulates Conscious Access
Radel, R., & Clément-Guillotin, C. (2012) · Psychological Science · doi:10.1177/0956797611427920 · Cited by 144 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Information bias in health research: definition, pitfalls, and adjustment methods
Althubaiti, A. (2016) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s104807 · Cited by 3,018 · see citing works
Four principles of explainable artificial intelligence
Phillips, P. J., Hahn, C. A., Fontana, P. C., Yates, A. N., Greene, K., Broniatowski, D. A., & Przybocki, M. A. (2021) · doi:10.6028/nist.ir.8312 · Cited by 262 · see citing works
Four Principles of Explainable Artificial Intelligence
Phillips, P. J., Hahn, C. A., Fontana, P. C., Broniatowski, D. A., & Przybocki, M. A. (2020) · doi:10.6028/nist.ir.8312-draft · Cited by 136 · see citing works
Thinking forensics: Cognitive science for forensic practitioners
Edmond, G., Towler, A., Growns, B., Ribeiro, G., Found, B., White, D., Ballantyne, K., Searston, R. A., Thompson, M. B., Tangen, J. M., et al. (2017) · Science & Justice · doi:10.1016/j.scijus.2016.11.005 · Cited by 95 · see citing works
Recognizing and reducing cognitive bias in clinical and forensic neurology
Satya-Murti, S., & Lockhart, J. (2015) · Neurology Clinical Practice · doi:10.1212/cpj.0000000000000181 · Cited by 42 · see citing works
Garrett, B. L. (2020) · Annual Review of Criminology · doi:10.1146/annurev-criminol-011518-024739 · Cited by 35 · see citing works
Casu, M., Guarnera, L., Caponnetto, P., & Battiato, S. (2024) · Forensic Science International: Digital Investigation · doi:10.1016/j.fsidi.2024.301795 · Cited by 30 · see citing works
Biased and biasing: the hidden bias cascade and bias snowball effects
Dror, I. E. (2025) · Behavioral Sciences · doi:10.3390/bs15040490 · Cited by 29 · see citing works
Self-citationThompson, W. C. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2301844120 · Cited by 20 · see citing works
Growns, B., & Kukucka, J. (2021) · Applied Cognitive Psychology · doi:10.1002/acp.3800 · Cited by 20 · see citing works
Self-citationForensic DNA Interpretation and Human Factors: Improving the Practice Through a Systems Approach
Taylor, M. K. (2024) · doi:10.6028/nist.ir.8503 · Cited by 14 · see citing works
- Authors
- Kassin, S. M., Dror, I. E., & Kukucka, J.
- Affiliations
- John Jay College of Criminal Justice (US)
- University College London (GB)
- Venue
- Journal of Applied Research in Memory and Cognition
- Published
- 2013-03
- Volume
- 2
- Issue
- 1
- Pages
- 42-52
- ISSN
- 2211-3681 (ISSN-L)
- 2211-369X
- OpenAlex
- W2110516497
- Cited by
- 634
- References
- 7
- Citations by year
- 2026: 39 · 2025: 43 · 2024: 57 · 2023: 46 · 2022: 47 · 2021: 59 · 2020: 65 · 2019: 74 · 2018: 37 · 2017: 42 · 2016: 34 · 2015: 42 · 2014: 25 · 2013: 24
- FWCI
- 61.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Applied Research in Memory and Cognition (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- National Institute of Standards and Technology
- Federal Bureau of Investigation
- US Department of Defense (N41756-10-C-3382)
- City University of New York
- Topics
- Social Sciences › Psychology › Social Psychology › Deception detection and forensic psychology
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
- Social Sciences › Law › Legal processes and jurisprudence
- Keywords
- contextual bias, primacy effect, expectancy effects, observer effect, fingerprint recognition, confession, eyewitness memory, expert opinion, judicial decision-making
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review