Inconclusive decisions and error rates in forensic science.

In recent years, there has been discussion and controversy relating to the treatment of inconclusive decisions in forensic feature comparison disciplines when considering the reliability of examination methods and results. In this article, we offer a brief review of the various viewpoints and suggestions that have been recently put forth, followed by a solution that we believe addresses the treatment of inconclusive decisions. We consider the issues in the context of and as two distinct concepts, both of which are necessary for the determination of reliability.

Registered reports in forensic science.

Research assessing the validity and reliability of many forensic science disciplines has been published; however, the quality of this research varies depending on the methodologies employed. This was a major point of contention with the United States' President's Council of Advisors on Science and Technology, who recognized the existing literature but found the majority lacking because of methodological issues. Questionable scientific methodologies have undermined the forensic science community's ability to defend the scientific foundations and examination protocols used to examine evidence in criminal cases.

Machine learning algorithms in forensic science: A response to Morrison et al. (2022).

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.

Probabilistic reporting and algorithms in forensic science: Stakeholder perspectives within the American criminal justice system.

In recent years, there have been efforts to promote probabilistic reporting and the use of computational algorithms across several forensic science disciplines. Reactions to these efforts have been mixed-some stakeholders argue they promote greater scientific rigor whereas others argue that the opacity of algorithmic tools makes it challenging to meaningfully scrutinize the evidence presented against a defendant resulting from these systems. Consequently, the forensic community has been left with no clear path to navigate these concerns as each proposed approach has countervailing benefits and risks.

Implementation of algorithms in pattern & impression evidence: A responsible and practical roadmap.

Over the years, scientific and legal scholars have called for the implementation of algorithms (e.g., statistical methods) in forensic science to provide an empirical foundation to experts' subjective conclusions.