In recent years, \u2018black box\u2019 studies in forensic science have emerged as the preferred way to provide information about the overall validity of forensic disciplines in practice. These studies provide aggregated error rates over many examiners and comparisons, but errors are not equally likely on all comparisons. Furthermore, inconclusive responses are common and vary across examiners and comparisons, but do not fit neatly into the error rate framework. This work introduces Item Response Theory (IRT) and variants for the forensic setting to account for these two issues. In the IRT framework, participant proficiency and item difficulty are estimated directly from the responses, which accounts for the different subsets of items that participants often answer. By incorporating a decision-tree framework into the model, inconclusive responses are treated as a distinct cognitive process, which allows inter-examiner differences to be estimated directly. The IRT-based model achieves superior predictive performance over standard logistic regression techniques, produces item effects that are consistent with common sense and prior work, and demonstrates that most of the variability among fingerprint examiner decisions occurs at the latent print evaluation stage and as a result of differing tendencies to make inconclusive decisions.<\/p>\n","protected":false},"excerpt":{"rendered":"
In recent years, \u2018black box\u2019 studies in forensic science have emerged as the preferred way to provide information about the overall validity of forensic disciplines in practice. These studies provide […]<\/p>\n","protected":false},"author":5,"featured_media":4942,"menu_order":0,"comment_status":"open","ping_status":"open","template":"","portfolio_category":[65],"portfolio_tag":[58,61],"acf":[],"yoast_head":"\n