Today I released the latest white paper in the Forensic Machine Learning Framework (FMLF) series:
Machine Learning as Forensic Evidence in Court.

This study addresses a rapidly emerging issue in digital and multimedia forensics: the increasing use of machine‑learning affected evidence in legal proceedings, often without disclosure, documentation, or the scientific foundations required for reliable interpretation. While ML components now appear across audio, image, video, and digital forensic tools, courts and practitioners rarely receive the information needed to evaluate how these systems operate or how they may influence evidentiary outcomes.

This white paper examines two domains of publicly available information: (1) the limited set of judicial opinions involving ML‑affected evidence, and (2) cross‑domain ML footprints in forensic tools and forensic‑adjacent tools based solely on vendor documentation. These findings are evaluated against established scientific and legal frameworks, including ISO/IEC 17025, NAS (2009), PCAST (2016), Federal Rule of Evidence 702, Daubert, Frye, and the doctrinal analysis of Grimm et al. (2021).

Across all domains, the study identifies consistent structural deficiencies: lack of transparency regarding ML involvement, absence of forensic‑specific validation or error‑rate data, no traceability of ML processing steps, and no reproducibility guarantees. The analysis shows that current ML‑affected evidence does not meet the scientific or legal expectations required for admissible forensic use.

The full paper is publicly available on OSF under a CC‑BY 4.0 license:

DOI: https://doi.org/10.17605/OSF.IO/Z5TEG

This release expands the foundational layer of the Forensic Machine Learning Framework, following earlier white papers on AI‑generated audio and video detection. Upcoming work in the series will include the Forensic ML Requirements Analysis study, beginning with neural‑network feasibility testing against the Framework’s five scientific‑legal pillars, as well as additional exploratory and validation studies across multimedia domains. More to come soon.