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The future of AI and the law

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Manage episode 493888504 series 2712286
Content provided by Stanford Engineering & Russ Altman and Stanford Engineering. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Stanford Engineering & Russ Altman and Stanford Engineering or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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

Have a question for Russ? Send it our way in writing or via voice memo, and it might be featured on an upcoming episode. Please introduce yourself, let us know where you're listening from, and share your question. You can send questions to [email protected].

Episode Reference Links:

Connect With Us:

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Improving outdated OCR systems using multimodal AI.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A tool to scan laws for outdated or excessive requirements.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(00:22:10) Outdated or Wasteful Regulations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

Connect With Us:

Episode Transcripts >>> The Future of Everything Website

Connect with Russ >>> Threads / Bluesky / Mastodon

Connect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook

  continue reading

331 episodes

Artwork

The future of AI and the law

The Future of Everything

184 subscribers

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Manage episode 493888504 series 2712286
Content provided by Stanford Engineering & Russ Altman and Stanford Engineering. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Stanford Engineering & Russ Altman and Stanford Engineering or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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

Have a question for Russ? Send it our way in writing or via voice memo, and it might be featured on an upcoming episode. Please introduce yourself, let us know where you're listening from, and share your question. You can send questions to [email protected].

Episode Reference Links:

Connect With Us:

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

Connect With Us:

Episode Transcripts >>> The Future of Everything Website

Connect with Russ >>> Threads / Bluesky / Mastodon

Connect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook

  continue reading

331 episodes

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