Joining Pam and Rich for this discussion are Professor Daniel Ho and RegLab Fellow Christie Lawrence, JD ’24 (MPP, Harvard Kennedy School of Government).

Dan is the founding director of Stanford’s RegLab (Regulation, Evaluation, and Governance Lab), which builds high-impact partnerships for data science and responsible AI in the public sector. The RegLab has an extensive track record partnering with government agencies like the Environmental Protection Agency, Internal Revenue Service, the U.S. Department of Labor, and Santa Clara County on prototyping and evaluating AI tools to make government more fair, efficient, and transparent. Building on this work, the RegLab also helps agencies strengthen AI governance and operationalize trustworthy AI principles.

Christie, a third-year JD student, worked with RegLab and Stanford’s Innovation Clinic on projects to advise DOL on responsible AI and development practices and to support the work with Prof. Ho on the National AI Advisory Committee, which advises the White House on AI policy. In this interview, we’ll learn about several RegLab projects—and the importance of helping government develop smart AI policy and solutions.

Connect:

• Episode Transcripts >>> Stanford Legal Podcast Website
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• Rich Ford >>>  Twitter/X
• Pam Karlan >>> Stanford Law School Page
• Stanford Law School >>> Twitter/X
• Stanford  Law Magazine >>> Twitter/X

Links:

• Dan Ho >>> Stanford Law School web page

[00:00:00 ] Chapter 1: Setting the Stage

• Mention of the rapid acceleration of technology and the release of ChatGPT.
• Highlighting the risks associated with AI, such as bias and privacy concerns.
• Discussion on the relationship between AI and governance, including recent developments in AI policy and governance.
• Mention of the Biden administration's executive order on AI and its implications.

[00:03:04 ] Chapter 2: The Role of Reg Lab and Collaboration with the IRS

• Explanation of the Reg Lab and its purpose.
• Discussion on the need for government agencies to modernize their technology infrastructure.
• Overview of the collaboration with the IRS to improve tax evasion detection using machine learning.
• Discovery of disparities in auditing rates and subsequent IRS reforms.
• Highlighting the intersection of AI, social justice, and government practices.

[00:09:12 ] Chapter 3: Student Perspective

• Christie Lawrence shares her experience working on AI policy at Stanford Law School.
• Discussion on bridging the gap between policy, law, and technology.
• Impactful work done by students in collaboration with government agencies.

[00:11:38 ] Chapter 4: AI and Social Justice

• Pam Karlan's experience with AI issues in the Justice Department's Civil Rights Division.
• Examples of algorithmic discrimination and its implications for social justice.
• Discussion on the challenges of addressing AI-related issues in government practices.

[00:23:55 ] Chapter 5: Future Directions

• Optimism about the future of AI governance and the recent executive order's impact.
• Anticipation of legislative proposals and state-level initiatives in AI regulation.
• Importance of maintaining an open innovation ecosystem and addressing talent gaps in government agencies.

[00:25:55 ] Chapter 6: Audience Questions