This program invites political science, policy, and law students to identify and research promising artificial intelligence (AI) use cases that address pressing public policy challenges. These “AI opportunities” may be emerging or established tools operating in sectors such as: 

• Health care
• Education
• Public safety
• Transportation
• Economic development
• Access to justice
• Government effectiveness 

Selected opportunities will be compiled and shared via the University of Texas School of Law. 

The objective is twofold: 

1. Help students develop structured expertise in applied AI governance.
2. Surface credible, policy-relevant AI tools that merit broader attention. 

An AI opportunity is a concrete use case in which artificial intelligence is being deployed to address a defined public policy problem.

Strong submissions typically involve: 

• A clearly identifiable AI developer or deploying institution
• A discernible public-facing impact (not merely internal automation)
• Evidence of implementation or testing
• A plausible connection to measurable policy outcomes 

Examples might include AI tools that: 

• Improve diagnostic accuracy in underserved communities
• Streamline eviction defense intake for legal aid organizations
• Reduce traffic fatalities through predictive modeling
• Improve benefits eligibility screening in government agencies 

The emphasis is on substance, real world use cases, and not hype. 

A. The AI Entity 

Document key institutional facts: 

• Where is the organization based?
• How long has it been operating?
• Approximate size (employees, funding, partnerships)?
• For-profit or nonprofit?
• What kinds of AI systems do they develop (e.g., large language models, predictive analytics, computer vision, decision-support systems)?
• Is the AI proprietary, open source, or licensed? 

The goal is to understand institutional credibility, technical maturity, and governance structure. 

B. The Opportunity Itself 

Explain the use case in policy-relevant terms: 

• What sector is being addressed?
• What specific problem is being targeted?
• What evidence exists of impact? (studies, pilots, audits, testimonials, independent evaluations)
• What is the target population or community?
• What institutions are involved (government agencies, hospitals, school districts, courts, etc.)?
• What press or public coverage has it received? 

You should distinguish between marketing claims and substantiated outcomes. 

C. Legal and Policy Implications 

Analyze the legal and governance dimensions: 

• Does this raise issues related to discrimination, due process, privacy, data governance, procurement law, or professional regulation?
• Are there relevant federal or state laws that directly implicate the tool?
• Are there regulatory uncertainties?
• Does this tool fit within existing administrative frameworks, or does it strain them?
• Does it represent a model that should be scaled, modified, or scrutinized? 

This section is meant to help policymakers and other AI stakeholders get a sense for any relevant legal and legislative issues pertaining to this opportunity.

Students are responsible for independently identifying opportunities. 

Recommended methods: 

• Monitoring sector-specific trade publications
• Reviewing government pilot programs
• Tracking university AI labs
• Following AI startups focused on public-sector applications
• Searching for public-private AI partnerships 

Before beginning research, you must verify that the opportunity has not already been submitted. A running list of prior submissions will be maintained for this purpose. 

What makes a strong submission?

Strong submissions: 

• Focus on a clearly defined use case
• Rely on verifiable sources
• Distinguish between speculation and evidence
• Identify concrete policy implications
• Avoid promotional tone 

Weak submissions: 

• Rely solely on press releases
• Describe “AI in general” without a specific deployment
• Omit legal analysis
• Duplicate prior submissions 

Submissions are assessed based on: 

1. Substantive quality – clarity, rigor, accuracy
2. Policy relevance – meaningful connection to public policy concerns
3. Originality – not previously submitted
4. Analytical depth – especially in the legal and governance section 

Students should approach this as professional research work. 

Participation strengthens: 

• AI literacy
• Regulatory analysis
• Administrative law application
• Technology policy research
• Professional memo drafting
• Evidence evaluation
• Institutional competence assessment 

Students gain experience evaluating AI not as abstraction, but as deployed infrastructure. 

In addition, approved opportunities are compiled and shared by the University of Texas School of Law.

The digest: 

• Highlights promising AI deployments
• Identifies emerging policy tensions
• Surfaces models for replication or scrutiny
• Contributes to ongoing national conversations about AI governance and opportunity 

This is a collaborative effort involving Brown, the University of Texas School of Law, the Berkman Klein Center, St. Thomas University College of Law, and the Abundance Institute.  This opportunity is limited to students enrolled at one of the below universities: 

• Brown University
• University of Texas School of Law
• UNLV
• Harvard
• St. Thomas 

This initiative is led by Kevin Frazier, Senior Fellow at the Abundance Institute, Director of the AI Innovation and Law Program at the University of Texas School of Law, a Senior Editor at Lawfare, and a Adjunct Research Fellow at the Cato Institute.

If you are interested in participating, please complete this Google form.