A five-step roadmap to closing the AI evaluation gap

Policy debates continue over how best to regulate artificial intelligence (AI) and harness its economic and other benefits while safeguarding society from its harms and risks.  For example, the European Union and China have opted to govern AI through different regulatory approaches, while the United States and some other countries have adopted different policy tools that they believe will better promote AI innovation. While countries and regions take different approaches, consensus is emerging across jurisdictions and among leading experts that there is an AI evaluation gap that must be promptly closed. 

The 2026 International AI Safety Report, prepared by more than one hundred experts and supported by more than 30 countries and multilateral organisations, explains that today’s AI evaluation techniques often fail to anticipate real-world performance.  This can occur when AI models produce overinflated test results or when AI testing environments materially differ from the real world.  Compounding the challenge, the “AI evidence dilemma” arises from the difficulties of assessing the risks of this rapidly evolving technology.

Boosting AI trust, diffusion, security and investment returns

Closing the AI evaluation gap will bring many benefits.  Sound AI evaluations would help increase understanding of AI’s performance and reliability and better inform decisions about its use.  This is critical since AI’s performance remains “jagged,” with some AI applications performing better than others.  

Helping people better understand AI’s reliability across contexts would enhance their trust in its appropriate use.  Similarly, reliable evaluations can also help buttress the security of AI systems.  All this, in turn, would help to support greater adoption and diffusion of secure and trusted AI applications and help organisations more fully reap the benefits of their AI investments.  

Reliable evaluation helps to reduce uncertainty for policymakers