Reliability and Admissibility of AI-Generated Forensic Evidence in Criminal Trials
This addresses the problem of ensuring fair and reliable use of AI evidence in criminal justice systems, but it is incremental as it builds on existing legal analysis without introducing new methods or data.
This paper tackles the problem of whether AI-generated forensic evidence meets legal reliability standards in criminal trials, finding that while AI tools can enhance evidence analysis, courts face challenges due to reproducibility deficits and lack of standardized validation, leading to variability in acceptance and risks of wrongful conviction.
This paper examines the admissibility of AI-generated forensic evidence in criminal trials. The growing adoption of AI presents promising results for investigative efficiency. Despite advancements, significant research gaps persist in practically understanding the legal limits of AI evidence in judicial processes. Existing literature lacks focused assessment of the evidentiary value of AI outputs. The objective of this study is to evaluate whether AI-generated evidence satisfies established legal standards of reliability. The methodology involves a comparative doctrinal legal analysis of evidentiary standards across common law jurisdictions. Preliminary results indicate that AI forensic tools can enhance scale of evidence analysis. However, challenges arise from reproducibility deficits. Courts exhibit variability in acceptance of AI evidence due to limited technical literacy and lack of standardized validation protocols. Liability implications reveal that developers and investigators may bear accountability for flawed outputs. This raises critical concerns related to wrongful conviction. The paper emphasizes the necessity of independent validation and, development of AI-specific admissibility criteria. Findings inform policy development for the responsible AI integration within criminal justice systems. The research advances the objectives of Sustainable Development Goal 16 by reinforcing equitable access to justice. Preliminary results contribute for a foundation for future empirical research in AI deployed criminal forensics.