法科学鑑定における専門家の熟達化と認知バイアス─認知心理学とヒューマンファクターの統合的枠組み─

抄録

This paper examines expertise development in forensic science from an integrated framework of cognitive psychology and human factors, with a particular focus on the role of cognitive biases in expert judgment. While forensic examiners are expected to provide objective and reliable evaluations, their decisions inevitably rely on human cognitive processes, which are subject to systematic biases and limitations.

Drawing on dual-process theory, the paper highlights the complementary roles of intuitive (system 1) and analytical (system 2) thinking in forensic decision-making. Expertise enables rapid and efficient pattern recognition through advanced system 1 processing; however, it also increases vulnerability to biases such as confirmation bias and contextual bias. System 2 can mitigate these risks by enabling deliberate reflection and critical thinking, but its effectiveness is constrained by cognitive load, time pressure, and organizational conditions.

The study further discusses the paradox of expertise: experienced examiners achieve higher overall accuracy yet remain susceptible to bias due to automated cognitive processes and overconfidence. This underscores the need to reconceptualize objectivity in forensic science as not only a function of scientific methods but also of managing cognitive risks.

To address these challenges, the paper emphasizes the importance of multi-layered interventions, including structured training, metacognitive skill development, and organizational measures such as Linear Sequential Unmasking and blind verification procedures. In addition, emerging AI technologies are considered as supportive tools that enhance transparency, facilitate error analysis, and promote reflective practice, rather than replacing human judgment.

Overall, expertise in forensic science should be understood as a dynamic process that integrates experience, reflection, and system-level design. Effective bias mitigation requires a combination of individual cognitive training and organizational workflow optimization, supported by human-AI collaboration.