Explainable Digital Forensics AI: Towards Mitigating Distrust in AI-Based Digital Forensics Analysis with Interpretable Models

Abiodun Abdullahi Solanke (University of Bologna)

Authors: Abiodun Abdullahi Solanke (University of Bologna)

DFRWS USA 2022

Abstract

The present level of skepticism expressed by courts, legal practitioners, and the general public over Artificial Intelligence (AI) based digital evidence extraction techniques has been observed, and understandably so. Concerns have been raised about closed-box AI models’ transparency and their suitability for use in digital evidence mining. While AI models are firmly rooted in mathematical, statistical, and computational theories, the argument has centered on their explainability and understandability, particularly in terms of how they arrive at certain conclusions. This paper examines the issues with closed-box models; the goals; and methods of explainability/interpretability. Most importantly, recommendations for interpretable AI-based digital forensics (DF) investigation are proposed.

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