Designing Safe AI Systems for Sensitive Domains

As AI systems are increasingly deployed in sensitive domains from medical diagnosis and drug discovery to financial decision-making, education and autonomous systems, ensuring safety becomes not just important—but essential. In these settings, even minor errors or unintended behaviors can lead to significant real-world consequences for end users.We’ll explore how teams are defining "AI safety" in their domains, and what practical, technical strategies they’re using to proactively build safety into both the model and the system architecture. This session will help us get a better understanding of how to frame, prioritize, and implement safety-first design principles in applied AI, especially when the cost of failure is high.