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

H Swofford¹, C Champod¹

¹School of Criminal Justice, Forensic Science Institute, University of Lausanne, Switzerland.

Forensic Science International. Synergy

|August 15, 2022

Summary

This summary is machine-generated.

This study defends its use of interview questions regarding computational algorithms in forensic science. The authors argue the questions were relevant and appropriate for criminal justice stakeholders.

Keywords:

Algorithms Artificial intelligence Forensic science Machine learning Statistical model

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Area of Science:

Forensic Science
Criminal Justice System
Computational Algorithms

Background:

Swofford & Champod (2022) explored interpretation and reporting practices in forensic science.
Interviews were conducted with diverse criminal justice stakeholders.
Morrison et al. (2022) critiqued the interview protocol's leading question and premise.

Purpose of the Study:

To address criticisms regarding a specific interview question used in forensic science research.
To validate the relevance and appropriateness of the interview premise concerning machine-learning methods.
To defend the methodology employed in Swofford & Champod (2022).

Main Methods:

Semi-structured interviews with criminal justice stakeholders.
Analysis of responses concerning interpretation and reporting practices.
Rebuttal of specific criticisms concerning interview question premises.

Main Results:

The study maintains that the interview question's premise was relevant and appropriate.
The authors disagree with Morrison et al.'s (2022) assertions about the question's leading nature.
The findings support the original interview protocol's validity.

Conclusions:

The interview methodology used in Swofford & Champod (2022) is defended.
The premise related to machine-learning opaqueness is deemed appropriate for criminal justice stakeholders.
Further discussion on computational algorithms in forensic science is warranted.