FAR.AI's second TIAP conference: Technical innovations for AI policy—what we heard

FAR.AI, together with the American Innovation Foundation, CNAS, and RAND, held the second Technical Innovations for AI Policy conference in Washington on March 30-31, with more than 200 policymakers, researchers, and technical experts attending. The core theme was that existing evaluations, standards, and safety frameworks are not keeping pace with AI governance needs. Anka Reuel noted that OpenAI's HealthBench never mapped test items to clinically recognized conditions and could not demonstrate that scores predict patient outcomes, and stated that a reported 90% accuracy for a welfare eligibility pre-screening agent could fall between 72% and 100% once uncertainty is accounted for. Ben Bucknall warned that the model version behind API outputs cannot be confirmed, proposing transparent disclosure and even cryptographic attestation based on trusted hardware enclaves as possible remedies.