AI Interpretability Grants (2026)

AI Interpretability Grants (2026) is sponsored by Schmidt Sciences. This program supports basic and applied research in AI interpretability, with a focus on understanding deceptive behaviors from AI models.

Center - AI - Schmidt Sciences Science of Trustworthy AI AI for Actionable Matter Modeling Humanities and AI Virtual Institute Accelerate Program for Scientific Discovery Supporting AI research and tools to accelerate breakthroughs across fields Opportunities for Funding Scaling AI Safety for a Multi-Agent World Science of Trustworthy AI Advance the science of AI to develop more robust, reliable, and safe AI-enabled systems, capable of realizing the beneficial promise of AI Enable AI to make significant contributions to humanity's greatest scientific challenges and opportunities Foster the creation of research platforms like datasets, experiments, evaluations and models that can both accelerate and measure the impacts of AI Contribute to the growth of a vibrant, diverse, and global scientific community focused on advancing AI The AI Agents pilot program supports researchers in academia and nonprofits who are exploring how multiple intelligent agents communicate and coordinate with one another.

AI2050 aims to support exceptional people working on the key opportunities and hard problems that are critical to get right for society to benefit from artificial intelligence. Science of Trustworthy AI The Science of Trustworthy AI program aims to support technical research that improves our ability to understand, predict, and control risks from frontier AI systems while simultaneously enabling their trustworthy deployment.

AI for Actionable Matter Modeling The AI for Actionable Matter Modeling pilot program aims to help connect computer models with real lab work by creating AI-powered simulation tools that make accurate, lab-ready predictions. Humanities and AI Virtual Institute The Humanities and Artificial Intelligence Virtual Institute (HAVI) fosters interdisciplinary collaborations to bridge the gap between AI capabilities and humanities research.

The AI at Work program aims to drive exceptional research into how AI adoption may influence the nature of work and the functioning of the labor market. Modern AI is built on two pillars: the transformer architecture and high-performance CMOS chips. While AI models have grown exponentially in capability, the hardware running them—standard CPUs and GPUs—is approaching the physical limits of fabrication.

This pilot program will catalyze “next-wave” AI hardware that is fundamentally different from conventional digital accelerators. Accelerate Program for Scientific Discovery The Cambridge Accelerate Program for Scientific Discovery provides young researchers with specialized training in AI techniques, equipping them with the skills they need to use machine learning and AI to power their research.

The ELICSIR Foundation aims to cultivate a new generation of exceptionally talented and motivated young computer scientists and to significantly improve Italy’s global reputation in the field of computing. Many advanced AI systems operate as “black boxes,” making it hard to understand or validate how they arrive at their answers, and limiting trust in their outputs.

The AI Interpretability pilot program supports research to make AI systems more interpretable and reliable, with a particular focus on detecting and preventing deceptive behaviors such as sycophancy and knowingly harmful advice. Penn State University | Feb 9, 2026 Q&A: Can humanities-driven AI reshape digital archive preservation and access? Could artificial intelligence save endangered archives?

A Kenyon College cohort aims to find out Schmidt Sciences awards over $3 million to study AI’s impact on jobs 2026 Humanities and AI Virtual Institute Development Awards The Harvard Gazette | Dec 30, 2025 Research Team Awarded Schmidt Sciences Grant for AI Humanities Project Schmidt Sciences awards $11M in grants to bring AI to humanities research The technical storage or access that is used exclusively for statistical purposes.

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