Jason Oxman President and Chief Executive Officer at Information Technology Industry Council | Official website
As the White House Office of Science and Technology Policy (OSTP) develops its 2025 Artificial Intelligence Research and Development Strategic Plan, the global tech trade association ITI has urged OSTP to prioritize a federal R&D strategy. This strategy should align with the Trump Administration's goals of enhancing national security and competitiveness, promoting human flourishing, and securing U.S. technological leadership.
ITI emphasized the importance of safeguarding AI R&D and prioritizing long-term investments to maintain America's competitive edge in the global AI race. "Government investment in high-risk, high-reward research areas has yielded revolutionary technological advances, so it remains critical that the Administration prioritize these long-term investments," ITI said in its comments.
The association suggested that federal attention should be directed towards continued research supporting standardized protocols for evaluating national security misuse risks of AI systems. They also highlighted the potential benefits of building upon initial AI Safety Institute guidance and focusing on developing consistent practices for advanced threat modeling and red-teaming of advanced AI systems.
"R&D should include a focus on creating additional tools and practices to promote the security and robustness of AI systems. Additionally, advancing research around human-AI interaction is useful," ITI continued. They noted that encouraging research into rethinking human-computer interactions in an edge AI world could be crucial, including AI-centric user interfaces, persistent AI, novel techniques for learning, and improving interpretability.
ITI outlined specific calls to action for policymakers: supporting continued investment in foundational and high-risk AI research across the full value chain; strengthening AI cybersecurity R&D by investing in relevant fields such as cyber-defense, data analytics, fraud detection, or adversarial machine learning; advancing standards and benchmarks for testing and evaluating AI systems through U.S. participation in global standard-setting bodies; promoting collaboration with international partners via initiatives like the International Network of AI Safety Institutes; investing in multi-disciplinary education to prepare future AI talent; and investing in advanced compute infrastructure like the National AI Research Resource to support AI R&D.
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