# kaal:claim:4941807-009

**Claim.** Concrete cases show the cost of AI opacity: Nvidia self driving cars that learn from human behavior might confuse the moon for a traffic light, and the DeepPatient project predicted disease onset accurately from medical records while offering no explanation for its predictions.

**Type.** empirical  **Support.** evidenced

**Holds when.**

- in deep learning systems deployed in driving and medical prediction

**Source quote.**

> For example, Nvidia's self-driving cars learn from human behavior but might confuse the moon for a traffic light, and the DeepPatient project accurately predicted disease onset from medical records without providing explanations for its predictions

**From.** Wulf A. Kaal, *AI Governance Via Web3 Reputation System* (2024), ORIGIN OF AI, page 6

**Cite as.** Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024). SSRN: https://ssrn.com/abstract=4941807

**Verify.** sha256 of source PDF `ab66c1e99a88da1fa36b0c6b536df5184231fe6aa427f3dd53287a4e0ac79853` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202024%20-%20AI%20Governance%20Via%20Web3%20Reputation%20System.pdf

**Failure mode.** unexplainable prediction  (family: ai-model-and-training-failure)

**Topics.** ai-and-agents, reputation

**Keywords.** black-box-ai, explainability, medical-ai, autonomous-vehicles, trust

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