AI-generated evidence: a guide for judges
As artificial intelligence transforms how evidence is created and presented, courts and legal professionals must adapt. This guidance features a webinar and two bench cards to help judges evaluate AI-generated content and ensure fair, informed decisions that preserve the integrity of judicial proceedings.
Who should read this?
- Judges & court administrators: Explore practical steps to evaluate AI-generated evidence and support technology-informed courtroom operations.
- Judicial educators & policy developers: Build curriculum and guidelines around AI use to future-proof courtroom procedures and evidence standards.
- Attorneys: Get insights into how courts may assess and deal with AI-enhanced or fabricated digital submissions.
Why this guidance matters
AI-generated evidence is becoming more common and more complex. Through our webinar and bench cards, we're helping judges and other court professionals by providing expert guidance to highlight the difference between useful technology and harmful manipulation.
Get resources
Bench card for acknowledged AI
FAQs
Top takeaways
Identify type of AI evidence
Courts must distinguish between "acknowledged" AI-generated evidence and "unacknowledged" evidence that is potentially altered to mislead, each of which demands different judicial considerations.
Ask the right questions
Each bench card provides judges with targeted questions about the source, chain of custody, metadata, and verification that help probe the credibility and integrity of the AI evidence presented.
Balance between use and risk
AI can clarify complex issues, but also unfairly sway jurors. Judges should carefully weigh the value of AI evidence against the risk of undue influence or prejudice.
Expert help may be essential
In cases involving technical complexity or ambiguous authenticity, appointing a neutral expert can help your court ensure just outcomes and informed decisions.
TRI/NCSC AI Policy Consortium
An intensive examination of the impact of technologies such as generative AI (GenAI), large language models, and other emerging, and yet-to-be developed tools.