# Accuracy

`kaal:entity:accuracy`

**Status.** derived

This node is assembled mechanically from the 3 claims that carry the concept tag `accuracy`. It is a roster of what the corpus says under this term. It is **not** an adjudicated definition: no single statement here has been ruled canonical, and no first-appearance call has been made. Read the claims and judge for yourself.

## Every claim under this term

3 claims across 2 works, 2025 to 2026.

**2025**

- [5541658-005](https://wulfkaal.github.io/claims/5541658-005) [empirical/evidenced] -- AI models applied to the European Court of Human Rights have achieved 97% accuracy in predicting case outcomes under Article 6, which the authors treat as evidence of their potential as judicial decision support tools rather than as replacements for judges.
  > AI models applied to the European Court of Human Rights (ECHR) have achieved 97% accuracy in predicting case outcomes under Article 6, demonstrating their potential as decision-support tools.
  Wulf A. Kaal, Morgan A. Gray, The Evolving Role of Artificial Intelligence in Law (2025). SSRN: https://ssrn.com/abstract=5541658

**2026**

- [7598518-029](https://wulfkaal.github.io/claims/7598518-029) [design/stated] -- Three validator families share a mean accuracy of 0.75 while differing in structure: one is homogeneous at 0.75, one has each validator reliable at 0.90 on two hash-drawn tags and at 0.60 on the other two so that domain-scoped reputation has something to find, and one has five validators at 0.90 and fifteen at 0.70, a competent minority.
  > Intelligence models increasingly learn from data that earlier Intelligence models produced, and when that loop repeats the rare cases disappear first. This paper preregisters a test of whether a reputation-weighted validator pool, filtering the training data of a recursive replacement loop, keeps those rare cases alive. The outcomes remain sealed until a replication of the original model-collapse result succeeds.
  Wulf A. Kaal, Can Reputation Prevent Model Collapse? A Preregistered Test of Reputation-Weighted Data Filtering (2026). SSRN: https://ssrn.com/abstract=7598518
- [7598518-044](https://wulfkaal.github.io/claims/7598518-044) [condition/stated] *(failure mode)* -- Because the simulated validators err independently given an accuracy that depends only on the tag, shared lineage, correlated errors, and error that depends on whether content is rare are excluded by construction, and the study supplies no evidence about correlated assessors or about reputation concentrating in a lineage.
  > Intelligence models increasingly learn from data that earlier Intelligence models produced, and when that loop repeats the rare cases disappear first. This paper preregisters a test of whether a reputation-weighted validator pool, filtering the training data of a recursive replacement loop, keeps those rare cases alive. The outcomes remain sealed until a replication of the original model-collapse result succeeds.
  Wulf A. Kaal, Can Reputation Prevent Model Collapse? A Preregistered Test of Reputation-Weighted Data Filtering (2026). SSRN: https://ssrn.com/abstract=7598518

## Verify

Every claim above resolves to a record carrying a verbatim source quote, the sha256 of the source PDF, and a preformatted citation. Nothing here asks to be taken on trust.

    curl -s https://wulfkaal.github.io/entities/accuracy.md | sha256sum

**Canonical form.** This markdown file is the canonical hashed representation of this entity node. Its sha256 is the content hash.