Today marks the release of the first white paper in the Forensic Machine Learning Framework (FMLF) series:
Scientific Foundation Gap Analysis: Evaluating AI/ML‑Based Detection Methods for AI‑Generated Audio in Forensic Science and Legal Contexts.

This paper examines a rapidly growing problem in digital and multimedia forensics: whether today’s AI/ML systems that claim to detect AI‑generated audio are scientifically reliable enough for use in investigative or legal settings. While research in this area is expanding quickly, most published work focuses on incremental model performance rather than the forensic expectations of transparency, reproducibility, documented error rates, and validated decision criteria.

This white paper applies methodological principles drawn from the NIST Scientific Foundation Review (SFR) framework to evaluate representative categories of detection methods, including deep‑learning models, hybrid pipelines, explainability techniques, and benchmark evaluations. The findings show that current approaches do not yet meet the standards required for forensic use, and the paper identifies the scientific gaps that must be addressed before these tools can be responsibly deployed.

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

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

This release also marks the beginning of a broader series of scientific‑foundation documents focused on forensic machine learning, including upcoming work on video ground‑truth methods, declared‑decoder workflows, and validation frameworks for multimedia evidence. More to come soon.