Law professor Daniel Ho says that the law is ripe for AI innovation, but a lot is at stake. Naive application of AI can lead to rampant hallucinations in over 80 percent of legal queries, so much research remains to be done in the field. Ho tells how California counties recently used AI to find and redact racist property covenants from their laws—a task predicted to take years, reduced to days. AI can be quite good at removing “regulatory sludge,” Ho tells host Russ Altman in teasing the expanding promise of AI in the law in this episode of Stanford Engineering’s The Future of Everything podcast
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Chapters:
(00:00:00) Introduction
Russ Altman introduces Dan Ho, a professor of law and computer science at Stanford University.
(00:03:36) Journey into Law and AI
Dan shares his early interest in institutions and social reform.
(00:04:52) Misconceptions About Law
Common misunderstandings about the focus of legal work.
(00:06:44) Using LLMs for Legal Advice
The current capabilities and limits of LLMs in legal settings.
(00:09:09) Identifying Legislation with AI
Building a model to identify and redact racial covenants in deeds.
(00:13:09) OCR and Multimodal Models
Improving outdated OCR systems using multimodal AI.
(00:14:08) STARA: AI for Statute Search
A tool to scan laws for outdated or excessive requirements.
(00:16:18) AI and Redundant Reports
Using STARA to find obsolete legislatively mandated reports
(00:20:10) Verifying AI Accuracy
Comparing STARA results with federal data to ensure reliability.
(00:22:10) Outdated or Wasteful Regulations
Examples of bureaucratic redundancies that hinder legal process.
(00:23:38) Consolidating Reports with AI
How different bureaucrats deal with outdated legislative reports.
(00:26:14) Open vs. Closed AI Models
The risks, benefits, and transparency in legal AI tools.
(00:32:14) Replacing Lawyers with Legal Chatbot
Why general-purpose legal chatbots aren't ready to replace lawyers.
(00:34:58) Conclusion
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