Who Is Responsible When AI Gets It Wrong?

When an AI product causes harm, the founder, developer and model provider can each point elsewhere. Who is actually answerable when a human-facing AI gets it wrong? Years of safety engineering on live productions showed Shannon that responsibility needs designing in from day one. Check who owns an AI product, its limits and where complaints escalate before you use it. A career spanning technology, research and live production for 15+ years keeps raising the same question. Testing and clear ownership must come first; a patch after launch reaches users first. Name the owner before launch, and nobody gets off the hook.

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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