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

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.

Keywords

• Forensic science
• Statistical model
• Machine learning
• Artificial intelligence
• Algorithms