A risk-based approach to cognitive bias in forensic science

AbstractOver the past decade, the potential impact of cognitive bias in forensic science has instigated much discussion and debate between academics, scientists and those in the justice sector. Evidence of bias influencing subjective decision-making across a range of forensic disciplines has been de...

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Veröffentlicht in:Science & justice 2019-09, Vol.59 (5), p.533-543
Hauptverfasser: Camilleri, Andrew, Abarno, Damien, Bird, Carolyne, Coxon, Anne, Mitchell, Natasha, Redman, Kahlee, Sly, Nicol, Wills, Stephen, Silenieks, Edmund, Simpson, Ellie, Lindsay, Heather
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Sprache:eng
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Zusammenfassung:AbstractOver the past decade, the potential impact of cognitive bias in forensic science has instigated much discussion and debate between academics, scientists and those in the justice sector. Evidence of bias influencing subjective decision-making across a range of forensic disciplines has been described in the literature. Forensic service organisations are being urged to address cognitive bias in subjective decision-making by designing processes or procedures to limit access to (irrelevant) contextual information or reduce dependence on cognitive functions. Although some laboratories have implemented bias mitigating strategies, with varying impact on operational efficiency, there has been no systematic assessment of the risk posed by cognitive bias. Forensic Science SA assessed the potential impact of bias on forensic interpretations across multiple disciplines, using a risk management framework. This process proved useful in assessing the effectiveness of existing bias mitigating strategies and identified the latent level of risk posed. While all forensic organisations should seek to implement bias limiting measures that are simple, cost-effective and do not adversely impact efficiency, using a risk-based approach has contextualised the limited benefit of introducing resource hungry measures, as postulated in the literature. That is not to suggest that forensic organisations should dismiss the potential influence of cognitive bias but they need to strike an appropriate balance between risk and return, as they do with any business risk.
ISSN:1355-0306
1876-4452
DOI:10.1016/j.scijus.2019.04.003