1. Why AI Matters in the Courtroom

• AI is already being used by lawyers and is being pitched to courts.
• There have been instances of fabricated citations in legal briefs and opinions due to AI tools.
• Judges and clerks need to understand what AI can and cannot do, how to spot risks, and when to trust AI outputs. 

2. Mission-Driven Approach

• The central question is not whether AI is powerful, but whether it advances the mission of the court: delivering justice justly, fairly, and efficiently.
• Technology should serve the needs of the court, not the other way around. 

3. Understanding the Bottleneck

• Courts face overwhelming caseloads, limited clerical support, and rising numbers of self-represented litigants.
• The real problem is delivering timely, quality justice with limited resources, not simply adopting AI for its own sake.

4. AI as a Potential Solution

• AI can help by organizing complex records, drafting routine orders, flagging procedural defects, and freeing up time for more complex judgment calls.
• However, AI is not always the right solution—other options like more clerks or procedural reforms may be preferable. 

5. How Large Language Models (LLMs) Work

• LLMs predict the next word in a sequence, learning from vast amounts of text.
• Their power comes from compressing knowledge into abstractions, which allows them to summarize, translate, and identify analogies, but not to memorize or search like a database.

6. The Hallucination Problem

• LLMs can generate plausible but false information (“hallucinations”) because they are optimized for fluency, not accuracy.
• This is a fundamental architectural issue, not just a user error, and can undermine justice if not properly managed. 

7. Reasoning vs. Mimicry

• LLMs sometimes appear to reason but also make mistakes no human would make.
• They operate using learned heuristics at scale—something between pattern-matching and true reasoning.

8. What Makes Judicial AI Different

• Judicial AI must be built with constraints: evidence-linking, procedural grounding, auditability, and multi-agent architecture.
• These constraints ensure accuracy, transparency, and reviewability, which are essential for serving the mission of the courts. 

9. Evaluating AI Tools

• Before adopting any AI tool, courts should ask:
  • What problem does this solve?
  • Does it advance justice, fairness, or efficiency?
  • What are the risks and alternatives?
• Red flags include lack of citation, vague answers, resistance to procedural constraints, and lack of transparency.
• Green flags include pinpoint citations, transparent reasoning, and clear articulation of limitations and mission advancement. 

10. Ethical Considerations

• Judges must maintain independence and avoid over-reliance on AI.
• When reviewing AI-generated content from lawyers, verify citations and be alert for unedited AI output.
• Good uses of AI include summarizing records, organizing facts, and flagging procedural defects; inappropriate uses include making final decisions without human review or anything requiring credibility determinations.

11. Questions for Vendors

• Focus on mission alignment, transparency, data handling, deployment, and clear limitations.
• Always ask what the tool does not do well and what human review is required. 

12. Looking Forward

• Judicial AI is rapidly being adopted, but best practices and legal questions are still evolving.
• The field is moving quickly, with new guidelines, ethics opinions, and case law emerging.

13. Key Takeaways

• Mission comes first: technology must serve justice, fairness, and efficiency.
• LLMs are powerful but require constraints for judicial use.
• Evaluate tools by their ability to solve real problems, advance the mission, and provide verifiable, auditable outputs.