kaal:claim:5541658-006

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.

Source quote, verbatim

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, p. 12
https://ssrn.com/abstract=5541658 · source PDF

Cite as

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

Holds when

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

Classification

empiricalsupport: evidencedempirical-evidence

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