An Explainable Pipeline for Machine Learning with Functional Data

Machine learning (ML) models have shown success in applications with an objective of prediction, but the algorithmic complexity of some models makes them difficult to interpret. Methods have been proposed to provide insight into these "black-box" models, but there is little research that focuses on supervised ML when the model inputs are functional data. In this work, we consider two applications from high-consequence spaces with objectives of making predictions using functional data inputs. One application aims to classify material types to identify explosive materials given hyperspectral computed tomography scans of the materials. The other application considers the forensics science task of connecting an inkjet printed document to the source printer using color signatures extracted by Raman spectroscopy. An instinctive route to consider for analyzing these data is a data driven ML model for classification, but due to the high consequence nature of the applications, we argue it is important to appropriately account for the nature of the data in the analysis to not obscure or misrepresent patterns. As such, we propose the Variable importance Explainable Elastic Shape Analysis (VEESA) pipeline for training ML models with functional data that (1) accounts for the vertical and horizontal variability in the functional data and (2) provides an explanation in the original data space of how the model uses variability in the functional data for prediction. The pipeline makes use of elastic functional principal components analysis (efPCA) to generate uncorrelated model inputs and permutation feature importance (PFI) to identify the principal components important for prediction. The variability captured by the important principal components in visualized the original data space. We ultimately discuss ideas for natural extensions of the VEESA pipeline and challenges for future research.

A reproducible pipeline for extracting representative signals from wire cuts

We propose a reproducible pipeline for extracting representative signals from 2D topographic scans of the tips of cut wires. The process fully addresses many potential problems in the quality of wire cuts, including edge effects, extreme values, trends, missing values, angles, and warping. The resulting signals can be further used in source determination, which plays an important role in forensic examinations. With commonly used measurements such as the cross-correlation function, the procedure controls the false positive rate and false negative rate to the desirable values as the manual extraction pipeline but outperforms it with robustness and objectiveness.

Revolutionizing Forensic Toolmark Analysis: An Objective and Transparent Comparison Algorithm

Forensic toolmark comparisons are currently performed subjectively by humans, which leads to a lack of consistency and accuracy. There is little evidence that examiners can determine whether pairs of marks were made by the same tool or different tools. There is also little evidence that they can make this classification when marks are made under different conditions, such as different angles of attack or direction of mark generation. We generate original toolmark data in 3D, extract the signal from each toolmarks, and train an algorithm to compare toolmark signals objectively. We find that toolmark signals cluster by tool, and not by angle or direction. That is, the variability within tool, regardless of angle/direction, is smaller than the variability between tools. The known-match and known-non-match densities of the similarities of pairs of marks have a small overlap, even when accounting for dependencies in the data, making them a useful instrument for determining whether a new pair of marks was made by the same tool. We provide a likelihood ratio approach as a formal method for comparing toolmark signals with a measure of uncertainty. This empirically trained, open-source method can be used by forensic examiners to compare toolmarks objectively and thus improve the reliability of toolmark comparisons. This can, in turn, reduce miscarriages of justice in the criminal justice system.

Hierarchical Decision Ensembles- An inferential framework for uncertain Human-AI collaboration in forensic examinations

Forensic examination of evidence like firearms and toolmarks, traditionally involves a visual and therefore subjective assessment of similarity of two questioned items. Statistical models are used to overcome this subjectivity and allow specification of error rates. These models are generally quite complex and produce abstract results at different levels of the analysis. Presenting such metrics and complicated results to examiners is challenging, as examiners generally do not have substantial statistical training to accurately interpret results. This creates distrust in statistical modelling and lowers the rate of acceptance of more objective measures that the discipline at large is striving for. We present an inferential framework for assessing the model and its output. The framework is designed to calibrate trust in forensic experts by bridging the gap between domain specific knowledge and predictive model results, allowing forensic examiners to validate the claims of the predictive model while critically assessing results.