# kaal:claim:4855607-004

**Claim.** Deep learning models adapt to changes in data distribution far less readily than human learning does, which limits their reliability once the operating environment diverges from the training data.

**Type.** failure  **Support.** evidenced

**Holds when.**

- settings where the data distribution shifts after training

**Source quote.**

> Furthermore, deep learning models are not as adaptable to changes in data distribution as human learning, which can adapt more quickly to new situations and contexts.

**From.** Wulf A. Kaal, *How AI Models are Optimized Through Web3 Governance* (2024), Model Overview: Deep Learning Models, page 7

**Cite as.** Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607

**Verify.** sha256 of source PDF `eb0b3e62374b45a8fa888c6bde9725e606bcb46cf4b5e74a6e851d9f25099113` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202024%20-%20How%20AI%20Models%20are%20Optimized%20Through%20Web3%20Governance.pdf

**Failure mode.** Distribution shift brittleness  (family: ai-model-and-training-failure)

**Topics.** research-methods

**Keywords.** deep-learning, distribution-shift, generalization, adaptability, robustness

**Related claims.**

- generalizes: https://wulfkaal.github.io/claims/5095633-010

**Canonical form.** This markdown file is the canonical hashed representation of the claim. Its sha256 is the content hash used for attestation.