Artificial intelligence (AI) is no longer just assisting the legal profession but transforming it – including by shaping how disputes are resolved. In arbitration, algorithms are already used to select arbitrators, analyze documents, and draft procedural orders. The essential question now is how AI will be governed in arbitration.

The America’s AI Action Plan (AAAP), released by the White House in 2025, offers an answer. Although the plan does not mention arbitration or smart contracts, it provides a national strategy for trustworthy and accountable AI. Its principles – transparency, human oversight, safety, and adaptive governance – can guide the design, use, and regulation of AI in dispute resolution.

Reframing the Question

Debates about AI arbitration often center on the legitimacy of machines to fairly and legally decide disputes. Yet the AAAP invites a more practical inquiry –  how does the AAAP extend to AI arbitration, and how can AI arbitration be responsibly implemented under the same federal principles that govern AI in the U.S. more broadly?

Why Arbitration Is the Testing Ground

Arbitration provides the ideal setting to strike a balance between innovation and fairness. It is a private and flexible process shaped by party autonomy, allowing participants to design their own procedures and adopt new tools as needed. It also has an international reach. Awards made in one country are routinely recognized and enforced in another under the New York Convention.

This dual character – contractual freedom combined with global enforceability –  makes arbitration an ideal environment for integrating AI responsibly. If arbitral institutions in the United States adopt AI tools guided by the AAAP’s principles, those practices could influence how other jurisdictions view automation in adjudication and enforcement.

Smart Contracts and Enforcement

Smart contracts are codes that automatically execute outcomes when conditions are met. These contracts may be legally binding if they meet legal requirements, or they may serve as enforcement mechanisms due to their conditional code characteristics]. The overlap with AI arbitration occurs mainly in two ways: (i) to resolve disputes that may arise from legally binding smart contracts, and (ii) to use smart contracts to enforce arbitration awards. For example, an AI arbitration renders its award and directly triggers an integrated smart contract to automatically transfer funds to the prevailing party.

Such efficiency, of course, comes with risks. Automation of enforcement can bypass human oversight and may conflict with legal safeguards. Under Article IV of the New York Convention, recognition and enforcement require a “duly authenticated original award.” Human validation – an arbitrator certifying or approving an AI-generated decision – satisfies this requirement and ensures due process. The AAAP’s principle of accountable autonomy thus supports maintaining human authorship even in highly automated environments.

The Plan’s Indirect Reach

Although the AAAP does not regulate arbitration, several of its initiatives indirectly shape it.

• NIST Standards: The plan directs the National Institute of Standards and Technology (NIST) to develop frameworks for safety, transparency, and reliability. These voluntary standards serve as benchmarks across various sectors. Arbitration institutions that align their AI tools with NIST guidance can demonstrate procedural credibility grounded in federal best practices.
• Regulatory Sandboxes: The plan encourages controlled experimentation, allowing new technologies to be tested under supervision. Arbitration could adopt similar pilot programs for AI-assisted drafting, scheduling, document analysis, or employing AI arbitrators, collecting feedback before full deployment. Such innovation aligns perfectly with the AAAP’s philosophy of learning through governance.

A State-Level Example: California’s SB 53

The AAAP’s influence is also visible in emerging state policy. California’s Transparency in Frontier Artificial Intelligence Act (SB 53) requires developers of advanced AI systems to publish detailed “Frontier AI Frameworks,” report safety incidents, and secure unreleased model weights from manipulation. These obligations turn transparency into practice through disclosure and accountability.

Arbitration institutions could follow a similar model. Publishing algorithmic audit reports, releasing anonymous performance statistics, or describing how AI tools comply with fairness standards. By doing so, they would reinforce public confidence and align private dispute resolution with broader U.S. governance trends.

From Federal Principles to Private Practice

Building on the AAAP, a set of Model Policies for AI arbitration and smart contract enforcement may be proposed. Each adapts a national principle to the realities of arbitration procedure. For example:

1. Transparency and Explainability: Institutions should disclose how AI systems operate and provide explanations for their outputs.
2. Human Oversight: Every AI-driven arbitration award must be reviewed and validated by a human arbitrator to meet authentication and enforcement standards.
3. Cybersecurity and Integrity: AI models and enforcement codes must be protected from manipulation or premature execution.
4. Reporting and Learning: Institutions should publish anonymous data on system accuracy, error rates, and user satisfaction to refine standards.
5. Cross-Border Coordination: Harmonize recognition and enforcement guidelines through cooperation with international arbitration and regulatory bodies.

Together, these policies turn the AAAP’s abstract commitments into actionable governance for the AI arbitration practices. They encourage innovation without sacrificing legal certainty or trust.

Why It Matters

AI arbitration will soon be widespread. Legal-tech platforms are already experimenting with AI-driven case management, document review, and predictive analysis. As these tools mature, they will inevitably become involved in decision-making. The question is whether this shift occurs within a framework that values fairness and accountability.

America’s AI Action Plan provides that framework. Extending its principles – transparency, human oversight, and responsible experimentation – to arbitration, the U.S. can shape global norms for digital justice. Properly applied, AI can improve efficiency and access.

This post comes to us from Bahadir Köksal at the Institute of Law and Economics, University of Hamburg. It is based on his recent paper, “America’s AI Action Plan, AI Arbitration, and Smart Contracts: The Policy Architecture of Automated Adjudication,” available here.