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A Forensic Science-Based Model for Identifying and Mitigating Forensic Mental Health Expert Biases.

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Cognitive biases can impact forensic expert decisions, even with objective data. This study applies a framework to forensic mental health assessments, proposing structured strategies to mitigate bias and enhance accuracy.

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Area of Science:

  • Cognitive Neuroscience
  • Forensic Psychology
  • Legal Medicine

Background:

  • Cognitive biases can influence expert decision-making in forensic science.
  • Forensic mental health evaluations are particularly susceptible to cognitive biases due to their subjective nature.
  • Itiel Dror's cognitive framework addresses biases in expert decision-making.

Purpose of the Study:

  • To adapt Dror's cognitive framework to forensic mental health assessments.
  • To explore how cognitive biases affect data collection and interpretation in this field.
  • To propose strategies for mitigating bias in forensic mental health evaluations.

Main Methods:

  • Application of Dror's cognitive framework to forensic mental health.
  • Analysis of bias influence on data collection and interpretation.
  • Proposal of mitigation strategies, including Linear Sequential Unmasking-Expanded (LSU-E).

Main Results:

  • Cognitive biases, influenced by contextual, motivational, and organizational factors, can affect forensic mental health evaluations.
  • Self-awareness alone is insufficient; structured, external strategies are necessary for bias mitigation.
  • The adapted framework offers a practical approach to reduce bias.

Conclusions:

  • Mitigating cognitive biases in forensic mental health requires systematic, external interventions.
  • Implementing strategies like LSU-E can improve the fairness and accuracy of assessments.
  • Dror's model provides a valuable tool for understanding and addressing bias in forensic mental health.