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Benchmarking AI: The Missing Link in U.S. Foreign Policy

The Trump administration’s AI Action Plan overlooks critical foreign policy benchmarks. Discover how rigorous testing can build trust and ensure AI’s role in...

July 24, 2025
By Visive.ai Team
Benchmarking AI: The Missing Link in U.S. Foreign Policy

Key Takeaways

  • The Trump administration’s AI Action Plan lacks foreign policy-specific benchmarking, crucial for AI trust in national security.
  • Rigorous benchmarking is essential for validating AI systems in high-stakes, high-uncertainty environments.
  • International collaboration on benchmarking standards can enhance U.S. strategic credibility and allied cohesion.

Benchmarking AI: The Missing Link in U.S. Foreign Policy

The Trump administration’s new plan on artificial intelligence, *Winning the AI Race: America’s AI Action Plan*, envisions AI as a cornerstone of U.S. geopolitical and economic dominance. However, a critical component is conspicuously absent: foreign policy-specific benchmarking. This oversight could undermine the plan’s ambitious goals, particularly in national security and strategic decisionmaking.

The Importance of Trustworthy AI

The success of the AI Action Plan hinges on the development of trustworthy, validated AI systems. These systems must be rigorously tested for performance under foreign policy stressors, such as countering Chinese influence, securing AI infrastructure, and exporting trusted models to allies. The plan calls for multiple agencies, including the National Institute of Standards and Technology (NIST), the Department of Energy, and the National Science Foundation, to lead in developing testbeds and standardized measurement science. Yet, foreign policy is notably absent from the list of sectors these testbeds will cover.

High-Stakes, High-Uncertainty Environments

Foreign policy decisionmaking is characterized by high stakes and high uncertainty. Consider a team at the Department of State advising on countering Chinese gray zone activity in the South Pacific. AI models must be benchmarked to ensure they provide reliable, contextually relevant insights. Without this foundation, the models risk returning flawed or even false insights, potentially undermining statecraft and national security.

Key Benefits of Benchmarking

  1. Trust and Accountability: Benchmarking establishes a foundation of trust by validating AI systems’ performance and reliability. This is crucial for ensuring that AI systems align with human objectives and maintain accountability.
  2. Bias Reduction: Rigorous testing helps identify and mitigate biases in AI models, which are particularly concerning in high-stakes environments like foreign policy. Models must be refined to ensure they do not reproduce biases in high-context, high-uncertainty settings.
  3. Iterative Improvement: Benchmarking is an iterative process that allows for continuous improvement. By setting clear, measurable standards, policymakers and technologists can progressively validate AI systems’ decision-making performance in controlled settings before broader implementation.

Building a Foreign Policy AI Evaluation Ecosystem

To operationalize the AI Action Plan’s ambition, the administration must expand the scope of model evaluation to include foreign policy-specific tasks and stressors. This begins by directing NIST, in collaboration with the Department of State, Department of Defense, and other key agencies, to incorporate foreign policy benchmarks into its evaluation agenda. These benchmarks should test large language models (LLMs) in critical foreign policy decisionmaking, such as analyzing escalation dynamics in great power competition and assessing diplomatic strategies.

Secure, Domain-Specific Testbeds

The Department of Energy and National Science Foundation should fund the creation of secure, domain-specific testbeds tailored to national security and diplomatic use cases. These testbeds should support evaluations that mirror modern foreign policy, including negotiations over Ukraine, great power competition in the Taiwan Strait, and economic statecraft to access resources in Africa.

International Collaboration and Standards

The United States must also take a leading role in shaping international benchmarking standards. Establishing shared benchmarking frameworks within alliances, such as NATO, is pivotal. Such coordinated efforts would ensure interoperability, facilitate collective responses to shared threats, and reinforce allied technological cohesion. The formation of public-private benchmarking consortia, involving civil society actors, academic institutions, and policy research centers, is essential to ensure evaluations are not captured by any single institutional perspective.

The Bottom Line

Rigorous benchmarking is the missing link in the Trump administration’s AI Action Plan. By incorporating foreign policy-specific benchmarks, the United States can build trustworthy AI systems that enhance national security and strategic decisionmaking. International collaboration and the development of robust, universally respected benchmarks will further solidify the U.S. position as a leader in responsible and trustworthy AI adoption on the global stage.

Frequently Asked Questions

Why is foreign policy-specific benchmarking important for AI systems?

Foreign policy decisionmaking involves high stakes and high uncertainty. Benchmarking ensures AI systems provide reliable, contextually relevant insights, reducing the risk of flawed or false insights that could undermine statecraft and national security.

How does benchmarking contribute to trust and accountability in AI systems?

Benchmarking establishes a foundation of trust by validating AI systems’ performance and reliability. It ensures that AI systems align with human objectives and maintain accountability, which is crucial for high-stakes environments like foreign policy.

What are the key benefits of incorporating foreign policy benchmarks in AI evaluation?

Key benefits include building trust, reducing biases, and facilitating iterative improvement. These benchmarks help identify and mitigate biases, ensure reliable performance, and allow for continuous refinement of AI systems.

How can international collaboration enhance AI benchmarking standards?

International collaboration ensures interoperability, facilitates collective responses to shared threats, and reinforces allied technological cohesion. Shared benchmarks and coordinated efforts strengthen the credibility and effectiveness of AI systems globally.

What role do secure, domain-specific testbeds play in AI evaluation for foreign policy?

Secure, domain-specific testbeds support evaluations that mirror modern foreign policy scenarios, such as negotiations over Ukraine and great power competition. These testbeds ensure that AI systems are tested in realistic, high-stakes environments.