Episode Details

Title

AI Knows a Version of You: Identity, Trust & the Fight to Be Understood

AI does not see you the way a person does.

In this roundtable episode of the AI Visibility Podcast, Jason T Wade talks with Jason Barnard, Jodi Koch, and Su Belagodu about what AI gets right about people, what it gets wrong, and what happens as machines become part of the discovery and recommendation process.

Jason Barnard starts with the entity problem. Before an AI system can recommend someone, it has to determine which person it is actually talking about. Shared names and overlapping identities make that harder than it looks. Roundtable on AI, Identity, and Ambiguity.docxDOCX

Su Belagodu gives a real example: AI once attributed a Dubai conference appearance to her because it appears to have confused her with another person sharing her surname who also worked in AI governance. Roundtable on AI, Identity, and Ambiguity.docxDOCX

The group then moves into a larger question: can you influence how AI understands you?

Jason Barnard argues that clarity and consistency matter. Su adds that AI outputs remain probabilistic, but repeated, coherent signals make it easier for systems to associate the right information with the right entity. Roundtable on AI, Identity, and Ambiguity.docxDOCX

Jodi Koch brings the discussion into interior design. She uses AI to help clients visualize options faster, but her experience also exposes the limits of machine output: an AI-generated design can look convincing while being completely impractical in the actual room. Roundtable on AI, Identity, and Ambiguity.docxDOCX

The conversation also examines the shift from traditional search to AI-driven recommendation. Instead of presenting ten links and asking the user to decide, AI systems increasingly synthesize information and narrow the choice themselves. Roundtable on AI, Identity, and Ambiguity.docxDOCX

The question is no longer only whether you can be found.

It is whether the machine understands the right version of you.

• AI identity and entity ambiguity
• Shared names and mistaken identity
• What AI gets wrong about people
• Probabilistic AI answers
• Digital consistency and corroboration
• Search versus AI recommendation
• Human judgment in AI systems
• AI agents and automation
• Expertise versus generated output
• AI in interior design
• Trust and verification
• Personal brand and machine understanding
• Why clarity comes before recommendation

Jason Barnard works through Kalicube on how Google and AI systems understand, represent, and recommend people and brands. His focus in the conversation is entity identity, ambiguity, digital consistency, and shaping machine understanding.

Jodi Koch is an interior designer with more than 22 years of experience and host of the Designing in 5D podcast. She uses AI to accelerate visualization and client communication while relying on professional experience to judge what will actually work in the physical world. Roundtable on AI, Identity, and Ambiguity.docxDOCX

Su Belagodu works in AI adoption, advisory, education, and human-in-the-loop system design. She advises AI startups, teaches organizations how to move beyond pilot projects, and focuses on keeping human judgment in AI systems where it matters. Roundtable on AI, Identity, and Ambiguity.docxDOCX

Jason T Wade is an AI Visibility Architect, founder of BackTier and NinjaAI, and host of the AI Visibility Podcast.

His work focuses on how AI systems discover, understand, classify, cite, include, and recommend people, companies, products, and ideas.

Jason Barnard / Kalicube