# Accuracy metrics

`kaal:entity:accuracy-metrics`

**Status.** derived

This node is assembled mechanically from the 2 claims that carry the concept tag `accuracy-metrics`. 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

2 claims across 1 works, 2025 to 2025.

**2025**

- [5541658-006](https://wulfkaal.github.io/claims/5541658-006) [empirical/evidenced] -- Support Vector Machine models reached 96.9% accuracy in classifying semantic biases in judicial judgments on the Chinese AI and Law dataset, outperforming Naive Bayes, multi-layer perceptron, and K-nearest neighbor classifiers.
  > as demonstrated by a study on the Chinese AI and Law (CAIL) dataset where SVM models reached 96.9% accuracy in classifying semantic biases in judicial judgments.
  Wulf A. Kaal, Morgan A. Gray, The Evolving Role of Artificial Intelligence in Law (2025). SSRN: https://ssrn.com/abstract=5541658
- [5541658-011](https://wulfkaal.github.io/claims/5541658-011) [normative/argued] -- Accuracy alone is insufficient for legal AI: a model must also be explainable before its outputs can be trusted in judicial settings.
  > Accuracy alone is insufficient without explainability.
  Wulf A. Kaal, Morgan A. Gray, The Evolving Role of Artificial Intelligence in Law (2025). SSRN: https://ssrn.com/abstract=5541658

## 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-metrics.md | sha256sum

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