Safety Is Not a Tax: Scaling Autonomy Without Surrendering Guarantees

Speaker: Jaime Fernández Fisac, Assistant Professor, Princeton University

Date: Oct. 9

Time: 2-3 p.m.

Q&A: 3-3:30 p.m.

Location: KEC 1001

Zoom

Title: Safety Is Not a Tax: Scaling Autonomy Without Surrendering Guarantees

Abstract: The promise of robotic and AI systems hinges on their ability to operate safely in complex settings and around people. From delivery drones to generative AI assistants, today's systems remain far from the reliability we take for granted in bridges, elevators, and airplanes. As they enter public use, two questions become urgent: what assurances should society demand from them, and must those assurances come at the cost of capability?

This talk presents a unified safety foundation to equip autonomous systems with auditable assurances within a specified operational design domain. We first consolidate the guarantees of previously disparate safety filters—runtime schemes that monitor intended actions and intervene to preempt harm—under a general theorem that lets us combine their distinctive features without losing certifiability. This yields a simple recipe that combines adversarial learning with predictive runtime reasoning to scale these safeguards beyond classical limits, from legged locomotion to high-speed racing.

We then turn to long-tail safety in autonomous driving and show how game-theoretic planners can adaptively guard against rare critical events without becoming prohibitively conservative. The same principle reveals a structural safety–performance separation in reinforcement learning, enabling optimal performance with zero violations. Across these settings, formal and empirical results show that strong guarantees need not come at the cost of capability. Finally, we demonstrate how this reasoning can improve safety in frontier AI systems, from proactive guardrails that steer interactions away from downstream harm to alignment pipelines that neutralize hidden feedback loops rewarding the manipulation of human beliefs. The talk ends with a vision for general “safety compilers” that let designers and regulators automatically translate safety specifications into enforcement and evaluation mechanisms.

Bio: Jaime Fernández Fisac is an Assistant Professor of Electrical and Computer Engineering at Princeton University, where he directs the Safe Robotics Laboratory. His research combines control, game theory, and machine learning to scale rigorous safety guarantees to increasingly capable robots and AI systems. To bring his group’s safety-filter research to commercial deployment, he co-founded Vault Robotics, which was recently selected for the NJ AI Hub Accelerator. Prof. Fisac holds an Engineering Degree from Universidad Politécnica de Madrid, a Master’s in Aeronautics from Cranfield University, and a Ph.D. in Electrical Engineering and Computer Sciences from the University of California, Berkeley. Before joining Princeton, he was a Research Scientist at Waymo. His work has been featured in The Wall Street Journal, IEEE Spectrum, and New Scientist, and recognized with the NSF CAREER Award, Google Research Scholar Award, and Sony Focused Research Award. He was elected to the Council of the International Association for Safe and Ethical AI (IASEAI) in 2026 and served as Program Co-Chair of IASEAI’26, its first peer-reviewed conference.