Auditability in Digital Forensic AI: Ensuring Transparency in Legal Evidence Analysis

The accelerating integration of artificial intelligence into digital forensics workflows precipitates a fundamental epistemological tension: the forensic mandate for deterministic, reproducible, and legally defensible evidence chains collides directly with the stochastic, opaque, and probabilistically governed nature of modern machine learning architectures. This paper examines Audit AI for Digital Forensics the systematic application of algorithmic transparency mechanisms, interpretability frameworks, and formal verification protocols to AI-assisted evidence analysis as a critical emerging discipline situated at the tripartite intersection of computer science, jurisprudence, and information epistemology. Taxonomy-The taxonomy of this domain bifurcates along two principal axes. The first is functional scope: encompassing artifact recovery, timeline reconstruction, network traffic analysis, malware attribution, and multimedia authentication each domain presenting distinct computational challenges to auditability. The second axis is transparency modality: ranging from ante hoc transparency (architectures designed with interpretability as a first-class constraint) to post hoc explainability (retrospective interrogation of black-box model decisions via surrogate methods). Within this framework, three structural pillars are identified as the foundational load-bearing elements of this analysis: (I) the formal architecture of audit trails and chain-of-custody preservation in AI pipelines, (II) the mathematical underpinnings of explainability methods and their forensic validity thresholds, and (III) the adversarial robustness of AI forensic tools against deliberate obfuscation and model-poisoning attacks. These three pillars are examined with vertical precision rather than horizontal breadth, as the forensic stakes demand architectural rigor over taxonomic comprehensiveness.