Science of Trustworthy AI - Schmidt Sciences

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 Science of Trustworthy AI

 
 

 

 
 
 
 
 
 
 
 Advancing the Fundamental Science of Trustworthy AI

 
 The science of trustworthy AI is nascent. Today, many demonstrations of unsafe behavior are isolated, and mitigations do not generalize reliably across model families, deployment contexts, or capability regimes. Therefore, we aim to support basic technical research that improves our ability to understand, predict, and control risks from frontier AI systems while simultaneously enabling their trustworthy deployment. 

 
 
 
 
 
 

 
 

 
 
 
 The Challenge 
 

 
 Every day, AI technology is becoming more consequential. As a result, the impact of safety failures are potentially incredibly harmful.

 
 
 We need mature decision-relevant evaluations.

 
 Today’s evaluations often fail exactly where we need them most: under distribution shift, long-horizon interaction, tool use, and optimization pressure. Many tests are brittle, highly correlated, or easy to “train to”, and stylized scenarios can be misleading if they do not reflect deployment-like contexts. We need a rigorous science of evaluation with construct validity, predictive validity, and clear evidence standards for when results justify real-world decisions.

 
 

 
 The research capacity needed for trustworthy AI lags the pace of deployment.

 
 Frontier systems are being deployed rapidly, but the infrastructure for trustworthy assessment and oversight is not keeping pace—especially for large, ambitious projects that require substantial compute, multidisciplinary expertise, and time. Accelerating progress will require sustained support for high-ambition, field-shaping research rather than incremental work.

 
 

 
 Academics are underleveraged in trustworthy AI research.

 
 Currently, safety research for the largest AI models is primarily conducted by leading AI labs. Still, despite vast private capital flowing into AI development, commercial incentives for foundational, pre-product safety science are often weaker than incentives for capability and product improvements, especially when benefits are diffuse or long-horizon. Foundational advances in the science of trustworthy AI therefore function as a global public good, motivating targeted philanthropic support for academic and nonprofit researchers.

 
 

 
 

 
 

 
 
 
 Program Goals 
 

 
 
 
 
 
 
 Deepen our understanding of safety properties of AI systems

 

 
 
 
 
 Build a rigorous science of evaluation with construct and predictive validity 

 

 
 
 
 
 Advance trustworthy AI approaches resistant to obsolescence from fast-evolving technology 

 

 
 
 
 
 Support a global community of researchers advancing the science of trustworthy AI 

 

 
 
 

 
 
 
 Research Agenda 
 

 
 Our Science of Trustworthy AI and Scaling AI Safety for a Multi-Agent World research agendas organizes priorities around three connected aims: 

 
 
 Characterize and forecast misalignment in frontier AI systems

 
 Why frontier AI trai

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