ArticleForensic science international. Synergy2022
Machine learning algorithms in forensic science: A response to Morrison et al. (2022).
Article in Forensic science international. Synergy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- From understanding to justifying: Computational reliabilism for AI-based forensic evidence evaluation.Forensic science international. Synergy · 2024Article
- Perceptions of forensic scientists on statistical models, sequence data, and ethical implications for DNA evidence evaluations: A qualitative assessment.Forensic science international. Synergy · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In Swofford & Champod (2022), we report the results of semi-structured interviews to various criminal justice stakeholders, including laboratory managers, prosecutors, defense attorneys, judges, and other academic scholars, on issues related to interpretation and reporting practices and the use of computational algorithms in forensic science within the American criminal justice system. Morrison et al. (2022) responded to that article claiming the interview protocol used a leading question with a false premise relating to the opaqueness of machine-learning methods. We disagree with the assertions of Morrison et al. (2022) and contend the premise to the question was relevant and appropriate.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.