The Role of AI Governance in Digital Forensics: A Framework for Ethical and Reliable Investigations
Bibliographic record
Abstract
AI integration in digital forensic investigations creates a critical methodological conflict. AI models operate as interpretive black boxes while forensic science demands transparency, reproducibility, and evidentiary admissibility. Existing AI governance frameworks provide foundational ethical principles but lack of operationalizable guidance for forensic contexts where evidentiary standards, chain-of-custody requirements, and cross-jurisdictional privacy regulations must simultaneously be satisfied-leaving investigators without concrete controls for deploying AI systems that meet both scientific rigor and legal admissibility requirements. This study introduces the Forensic AI Governance Framework (FAIGF), the first domain-specific governance model that operationalizes AI ethics into actionable forensic controls through a unified fivepillar structure: transparency and explainability, accountability and oversight, privacy and data protection, continuous monitoring, and scientific rigor. FAIGF uniquely addresses three critical needs: structured mapping between AI transparency and forensic documentation standards, privacypreserving protocols harmonizing international regulations with evidence handling, and validation mechanisms ensuring AI-generated evidence meets scientific admissibility criteria. Framework applicability is validated through structured scenarios spanning cybercrime investigations and cross-border evidence exchange across diverse regulatory contexts including European and Middle Eastern jurisdictions. Comparative analysis demonstrates FAIGF bridges the operationalization gap between abstract AI governance principles and concrete forensic practice requirements applicable across global investigative contexts. This study provides the first operationalizable framework enabling responsible forensic AI deployment while preserving evidentiary integrity and data subject rights.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.102 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.010 | 0.093 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".