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 "name": "Streaming Ethical Ai Governance In Automated Financial Decision Systems Balancing  75619C7558",
 "text": "Ethical AI Governance in Automated Financial Decision Systems: Balancing Predictive Accuracy with Regulatory Compliance presents the following source proposition: The system demonstrates 40% faster convergence than existing bias-corrected models through novel gradient masking techniques. This proposition is pertinent to Kaal's source-bound claim that WDAGs allow new regulatory and ethical standards to be integrated into existing AI systems without overhauling the entire model architecture, which is what makes rapid legal adaptation feasible in sectors such as public safety and healthcare. The proposed response is an extension: the relationship should remain limited to the retrieved source proposition and the mapped Kaal claim unless fuller source review supports a broader conclusion.",
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  "identifier": "https://orcid.org/0009-0008-7840-1847"
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  "External evidence level: abstract indexed.",
  "Mapping review tier: moderate-confidence claim review.",
  "Primary mapping confidence: 0.3909.",
  "The source-to-claim mapping remains explicitly ambiguous and is published with that limitation."
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  "name": "Ethical AI Governance in Automated Financial Decision Systems: Balancing Predictive Accuracy with Regulatory Compliance",
  "url": "https://doi.org/10.36227/techrxiv.174957648.89017169/v1"
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  "citation": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607",
  "paper": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance",
  "authors": [
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