Integrating AI Systems in Criminal Justice: The Forensic Expert as a Corridor Between Algorithms and Courtroom Evidence

Background: Artificial intelligence is transforming forensic fingerprint analysis by introducing probabilistic demographic inference alongside traditional pattern matching. This study explores how AI integration reshapes the role of forensic experts from interpreters of physical traces to epistemic corridors who validate algorithmic outputs and translate them into legally admissible evidence. Methods: A conceptual proof-of-concept exercise compares traditional AFIS-based workflows with AI-enhanced predictive models in a simulated burglary scenario involving partial latent fingermarks. The hypothetical design, which does not rely on empirical validation, illustrates the methodological contrasts between physical and algorithmic inference. Results: The comparison demonstrates how AI-based demographic classification can generate investigative leads when conventional matching fails. It also highlights the evolving responsibilities of forensic experts, who must acquire competencies in statistical validation, bias detection, and explainability while preserving traditional pattern-recognition expertise. Conclusions: AI should augment rather than replace expert judgment. Forensic practitioners must act as critical mediators between computational inference and courtroom testimony, ensuring that algorithmic evidence meets legal standards of transparency, contestability, and scientific rigor. The paper concludes with recommendations for validation protocols, cross-laboratory benchmarking, and structured training curricula to prepare experts for this transformed epistemic landscape.