# kaal:claim:5095633-014

**Claim.** In fast-moving fields such as technology, law, and public policy, models trained on obsolete datasets fail to capture new trends, behaviors, or regulatory changes, which reduces their predictive and explanatory power.

**Type.** failure  **Support.** argued

**Holds when.**

- domains with rapid regulatory or behavioral change

**Source quote.**

> In rapidly evolving fields—technology, law, or public policy—models trained on obsolete datasets fail to capture new trends, behaviors, or regulatory changes, ultimately reducing their predictive and explanatory power.

**From.** Wulf A. Kaal, *Artificial Intelligence The Final Frontier* (2025), 2.1.5. Data Timeliness and Relevance, page 4

**Cite as.** Wulf A. Kaal, Artificial Intelligence The Final Frontier (2025). SSRN: https://ssrn.com/abstract=5095633

**Verify.** sha256 of source PDF `cbb484711f89bcefc9fc6a5730a1ed0a3f764d7999ad9b6f7d8ea05634c26c63` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202025%20-%20Artificial%20Intelligence%20The%20Final%20Frontier.pdf

**Failure mode.** data staleness  (family: data-quality-and-comparability)

**Topics.** law-and-legal-systems

**Keywords.** data-staleness, legal-informatics, model-relevance, policy

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