# kaal:claim:5541658-006

**Claim.** 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.

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

**Holds when.**

- the Chinese AI and Law (CAIL) dataset
- semantic bias classification task

**Source quote.**

> 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.

**From.** Wulf A. Kaal, Morgan A. Gray, *The Evolving Role of Artificial Intelligence in Law* (2025), Predictive Analytics, page 12

**Cite as.** Wulf A. Kaal, Morgan A. Gray, The Evolving Role of Artificial Intelligence in Law (2025). SSRN: https://ssrn.com/abstract=5541658

**Verify.** sha256 of source PDF `e543a2d698fcd522d4d02e034cc9ee1344d0015d2c824b40b9e05ab7c0728c60` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20and%20Gray%20-%202025%20-%20The%20Evolving%20Role%20of%20Artificial%20Intelligence%20in%20Law.pdf

**Topics.** empirical-evidence

**Keywords.** predictive-analytics, cail-dataset, supervised-learning, accuracy-metrics

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