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 "name": "Accuracy metrics",
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 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0000-0003-0757-275X"
 },
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   "@id": "https://wulfkaal.github.io/claims/5541658-006",
   "identifier": "kaal:claim:5541658-006",
   "text": "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.",
   "abstract": "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.",
   "citation": "Wulf A. Kaal, Morgan A. Gray, The Evolving Role of Artificial Intelligence in Law (2025). SSRN: https://ssrn.com/abstract=5541658",
   "datePublished": "2025",
   "claim_type": "empirical",
   "confidence": "evidenced",
   "is_failure_mode": false,
   "scope_conditions": [
    "the Chinese AI and Law (CAIL) dataset",
    "semantic bias classification task"
   ],
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   "status": "current"
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  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/5541658-011",
   "identifier": "kaal:claim:5541658-011",
   "text": "Accuracy alone is insufficient for legal AI: a model must also be explainable before its outputs can be trusted in judicial settings.",
   "abstract": "Accuracy alone is insufficient without explainability.",
   "citation": "Wulf A. Kaal, Morgan A. Gray, The Evolving Role of Artificial Intelligence in Law (2025). SSRN: https://ssrn.com/abstract=5541658",
   "datePublished": "2025",
   "claim_type": "normative",
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 "description": "2 claims in the published works of Wulf A. Kaal carry the concept tag 'accuracy-metrics'. Derived node: a roster, not an adjudicated definition."
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