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

arXiv: https://arxiv.org/abs/2111.01131 · HTML: https://mlsanity.com/paper/2111.01131/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 43 references that are in the map.
- First posted on arXiv 31 Oct 2021.

## Builds on (most cited first, 2 of 2)

- [Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs](https://mlsanity.com/paper/2004.11440/) (2020)
- [Lessons Learned from Designing an AI-Enabled Diagnosis Tool for Pathologists](https://mlsanity.com/paper/2006.12695/) (2020)

## Built on by (most cited first, 1 of 1)

- [Automated Assessment of Residual Plots with Computer Vision Models](https://mlsanity.com/paper/2411.01001/) (2024)

## Abstract (arXiv)

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.