When AI Weakens Evidence Traceability: Ethical Challenges for Credibility and Responsibility in Digital Forensics
Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
Abstract
Artificial intelligence (AI) is increasingly used in digital forensic workflows to support the analysis, interpretation, and reporting of large datasets. While this integration offers efficiency benefits, it also raises ethical concerns. This paper examines the weakening of evidence traceability in AI-assisted digital forensic workflows as a challenge in digital forensics and argues that reduced traceability threatens two core requirements of forensic practice: evidence credibility and responsibility attribution. Unlike conventional technical errors, traceability breakdowns may generate analytic statements that appear plausible but lack explicit grounding in identifiable forensic artifacts, thereby weakening the link between evidence and inference.
Through an ethics- and governance-focused analysis, the paper shows how AI-assisted workflows can undermine evidentiary trust, distribute responsibility across human and institutional actors, and produce accountability gaps that conflict with forensic norms. The analysis also examines human and institutional effects of AI use, including automation bias, institutional reliance on AI outputs, and the displacement of responsibility.
The paper concludes that technical validation alone cannot address these issues. Responsible use of AI in digital forensics requires clear human–AI role definition, procedures that support auditability and contestability, and governance frameworks that assign accountability across the AI lifecycle. These conditions are necessary to maintain the credibility and legitimacy of digital forensic evidence.
Keywords:
Artificial Intelligence; Digital Forensics; Evidence Traceability; Algorithmic Accountability; AI GovernanceData Availability Statement
Data sharing is not applicable to this article as the study is based on normative ethical analysis and literature review. No datasets were generated or analysed during the research.
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