Crown Nicole, Marquis Raymond, Kupferschmid Erich, Dziedzic Tomasz, Belic Diana, Kerzan Dorijan
Zurich Forensic Science Institute, Zurich, Switzerland.
School of Criminal Justice, University of Lausanne, Bâtiment BATOCHIME, UNIL-Sorge, Lausanne, Switzerland.
Forensic Sci Res. 2024 Sep 17;9(4):owae065. doi: 10.1093/fsr/owae065. eCollection 2024 Dec.
Like other pattern recognition disciplines, forensic handwriting examination relies on various human factors. Expert opinions in the field are based on visual analysis and comparison, and the evaluation of findings is generally conducted without reference to tabulated data. This high level of subjectivity may contribute to bias and error in the examination process. In this paper, we draw on our research and practical experience to discuss error mitigation on several levels, addressing both aspects of quality management and the individual responsibility of examiners. Because a good understanding of the concept of error is needed to communicate appropriately about this subject, definitions of error-related concepts are provided. We consider contextual information management essential to reduce the potential for cognitive bias in casework. To ensure completeness of findings and avoid omission errors, the use of checklists during a forensic handwriting examination is encouraged, and an exemplary checklist incorporating all the examination steps is provided. We consider the use of a logical reasoning approach to evaluate findings an important step towards robustness and transparency in the examiner's report. An independent, blinded peer review of the examination is recommended as a further key step in error mitigation. Regular participation in testing programmes and continuous training and education are essential to maintaining and improving competency at both individual and organizational levels. Finally, developments in the form of tabulated data and the use of algorithms are considered useful ways of increasing objectivity in the field and minimizing human error.
与其他模式识别学科一样,法医笔迹鉴定依赖于多种人为因素。该领域的专家意见基于视觉分析和比较,对鉴定结果的评估通常不参考表格数据。这种高度的主观性可能会导致鉴定过程中出现偏差和错误。在本文中,我们借鉴研究和实践经验,从多个层面讨论如何减少错误,涉及质量管理和鉴定人员个人责任两个方面。由于要恰当地交流这个主题需要对错误概念有很好的理解,因此提供了与错误相关概念的定义。我们认为上下文信息管理对于减少实际案件中认知偏差的可能性至关重要。为确保鉴定结果的完整性并避免遗漏错误,鼓励在法医笔迹鉴定过程中使用清单,并提供了一个包含所有鉴定步骤的示例清单。我们认为使用逻辑推理方法来评估鉴定结果是使鉴定人员报告更稳健和透明的重要一步。建议对鉴定进行独立、盲法同行评审,作为减少错误的进一步关键步骤。定期参与测试计划以及持续培训和教育对于在个人和组织层面维持和提高能力至关重要。最后,表格数据形式的发展以及算法的使用被认为是提高该领域客观性和尽量减少人为错误的有用方法。