AI thrives on data—but what happens when it starts averaging everything out?

In this episode of A Beginner’s Guide to AI, we explore why AI’s obsession with statistical averages can be a serious problem.

From healthcare to hiring algorithms, we break down how flattening data can erase crucial insights, reinforce biases, and lead to one-size-fits-all solutions that don’t actually fit anyone.

We’ll unpack real-world examples, including how AI in medicine has overlooked critical symptoms for underrepresented groups like people not living in cities, and why recommendation engines often fail to capture what makes you unique.

Plus, we’ll explain why an AI-designed cake—made entirely from “average” preferences—might be the worst dessert ever created.

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Want to get in contact? Write me an email: podcast@argo.berlin

This podcast was generated with the help of ChatGPT, Mistral, and Claude 3. We do fact-check with human eyes, but there still might be hallucinations in the output. And, by the way, it's read by an AI voice.

Music credit: Modern Situations by Unicorn Heads.

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