ReviewForensic sciences research2024
Error mitigation in forensic handwriting examination: the examiner's perspective.
Review in Forensic sciences research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Forensic document examination: a global snapshot.Forensic sciences research · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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