More on digital evidence exceptionalism: Critique of the argument-based method for evaluative opinions

This paper critically analyses and discusses the “Argument-Based Method for Evaluative Opinions” (ABMEO) recently proposed by Sunde and Franqueira in a paper published in Forensic Science International: Digital Investigation ( Sunde and Franqueira, 2023 ). According to its developers, this novel method allows one to produce evaluative opinions in criminal proceedings by constructing arguments. The method is said to incorporate concepts from argumentation and probability theory, while ensuring adherence to accepted principles of evaluative reporting, in particular the ENFSI Guideline for Evaluative Reporting in Forensic Science. While this sounds promising, our analysis of the ABMEO, as well as Sunde and Franqueira's account of a number of evidence-related concepts such as probative value (and its assessment), credibility, relevance, normativity, and probability, among others, reveals a number of fundamental problems that are indicative of digital evidence exceptionalism ; i.e. the idea that digital forensic science can somehow exempt itself from adhering to methodologically and scientifically rigorous evidence evaluation procedures. In this paper we explain why the ABMEO cannot and should not be considered as an appropriate complement, supplement or replacement for the existing reference framework for evaluative reporting in forensic science. In particular, we argue that the ABMEO is internally contradictory and tends to undermine the substantial progress made over the past two decades in the development and implementation of principles for the evaluative reporting of forensic science evidence.

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Publication Details

Journal
Forensic Science International Digital Investigation
Published
2025-02-18
DOI
https://doi.org/10.1016/j.fsidi.2025.301885
Primary Topic
Bayesian Modeling and Causal Inference
Type
article
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More on digital evidence exceptionalism: Critique of the argument-based method for evaluative opinions

Kyriakos N. Kotsoglou, Alex Biedermann
Forensic Science International Digital Investigation
Bayesian Modeling and Causal Inference
article

More on digital evidence exceptionalism: Critique of the argument-based method for evaluative opinions

Kyriakos N. Kotsoglou, Alex Biedermann
article en

Abstract

This paper critically analyses and discusses the “Argument-Based Method for Evaluative Opinions” (ABMEO) recently proposed by Sunde and Franqueira in a paper published in Forensic Science International: Digital Investigation ( Sunde and Franqueira, 2023 ). According to its developers, this novel method allows one to produce evaluative opinions in criminal proceedings by constructing arguments. The method is said to incorporate concepts from argumentation and probability theory, while ensuring adherence to accepted principles of evaluative reporting, in particular the ENFSI Guideline for Evaluative Reporting in Forensic Science. While this sounds promising, our analysis of the ABMEO, as well as Sunde and Franqueira's account of a number of evidence-related concepts such as probative value (and its assessment), credibility, relevance, normativity, and probability, among others, reveals a number of fundamental problems that are indicative of digital evidence exceptionalism ; i.e. the idea that digital forensic science can somehow exempt itself from adhering to methodologically and scientifically rigorous evidence evaluation procedures. In this paper we explain why the ABMEO cannot and should not be considered as an appropriate complement, supplement or replacement for the existing reference framework for evaluative reporting in forensic science. In particular, we argue that the ABMEO is internally contradictory and tends to undermine the substantial progress made over the past two decades in the development and implementation of principles for the evaluative reporting of forensic science evidence.

Forensic Science International Digital InvestigationVol. 53
Northumbria University (GB), Department of Justice (IE)
Reduced inequalities
Openalex Percentile: Top 99%
Bayesian Modeling and Causal Inference
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