An evaluation of novice, expert and supervised machine learning model classifications for forensic hair analysis

• Published in: Australian journal of forensic sciences Vol. 56; no. 5; pp. 551 - 565
• Main Authors: Airlie, Melissa, Robertson, James, Brooks, Elizabeth
• Format: Journal Article
• Language: English
• Published: Sydney Taylor & Francis 02.09.2024; Australian Academy of Forensic Sciences; Copyright Agency Limited (Distributor)
• Subjects: Analysis; Classification; DNA; Evaluation; forensic hair analysis; Forensic science; Forensic sciences; Genetic testing; Hair; Hair analysis; Learning algorithms; Machine learning; Reliability; Statistical analysis; Supervised learning; Technology assessment; validation; AUSTRALIA
• ISSN: 0045-0618; 1834-562X
• DOI: 10.1080/00450618.2023.2254337

An evaluation of forensic hair analysisbetween experts, novices and the recently developed machine learning platform, HairNet, was conducted to assess accuracy and reliability. Our hypothesis stated experts and the machine learning platform will outperform novices in classifications of hair as human or non-human and suitability for nDNA analysis based on specialist knowledge and from training of the model. Statistically significant differences between novices and experts were found and attributed to training and experience for more complex classifications. For more simplistic classifications, no statistically significant difference between the novice and the experts was found. HairNet proved responses similar to expert responses in all classifications. Encouraging feedback was received regarding the use of technology and machine learning. The utilization of technology undoubtedly holds great promise to become part of the forensic tool kit for improving the efficiency and reliability of forensic hair analysis and in research, education and competency testing.