What Opera, Clowning and Safety Engineering Teach About AI

Engineers judge technology on speed, but performers judge a room by its mood. What would an AI system do if it read the room the way a performer does? Imagine a chatbot that notices panic, then slows down and asks one gentle question instead of listing tips. From opera to live production, Shannon has come to see that reading the room comes before every decision. Plan for the worst case before you ship, rather than designing only for normal use. Reading the room is a habit; checking it before launch keeps the moment safe. Read the room first, and the product will do less harm when it counts.

About Shannon Harvey

Shannon Harvey, podcast guest

Founder & AI Safety Researcher

15+ years Founding and leading studios and research initiativesApple TV+ Virtual production leadership on Silo and HijackProduction Park Established its research labReflection Labs Founder, independent governance for human-facing AI

Shannon Harvey is a founder, researcher and innovation leader focused on AI safety, human behaviour and responsible technology. He is currently developing research and practical testing frameworks to help companies identify and reduce the emotional, psychological and social risks created by human-facing AI.

With 15+ years of experience spanning technology, immersive media, live production, research and academia, Shannon brings a unique perspective shaped by both systems engineering and a background in performance. He has led complex technical and creative projects, including virtual production for Apple TV+ productions such as Silo and Hijack, research at Production Park, and platform adoption work with disguise. He was also Course Leader for the BA (Hons) Visual Production degree at Backstage Academy, now the Academy of Live Technology.

Shannon originally trained and performed as an opera singer before moving into theatre, and large-scale live production. His experience of performance, audience interaction and safety engineering has shaped the way he thinks about AI: particularly the importance of understanding context, vulnerability, trust and what can happen when systems behave unexpectedly.

His current research explores the often-overlooked side of AI safety - what happens when people trust, depend on or emotionally engage with AI systems, particularly in high-trust and vulnerable situations.

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