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 "name": "Streaming Compliance By Design For Ai Driven Insurtech Operationalizing Regulatory 6885F04Ceb",
 "text": "Compliance-by-Design for AI-Driven Insurtech: Operationalizing Regulatory Governance via Explainable AI and Federated Learning presents the following source proposition: Federated Learning retains training-time privacy while a real-time Bias Interceptor enforces the Disparate Impact Ratio (DIR) per decision. This proposition is pertinent to Kaal's source-bound claim that Taken together, the transparency, decentralized decision making, and automated real time response properties of the proposed model make decentralized governance superior to AI driven supervision for secure, compliant, and efficient execution of AI agent transactions. 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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  "External evidence level: abstract indexed.",
  "Mapping review tier: moderate-confidence claim review.",
  "Primary mapping confidence: 0.3833.",
  "The source-to-claim mapping remains explicitly ambiguous and is published with that limitation."
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  "url": "https://doi.org/10.2139/ssrn.6440420"
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  "citation": "Wulf A. Kaal, How can we Best Monitor AI Agents (2025). SSRN: https://ssrn.com/abstract=5245185",
  "paper": "Wulf A. Kaal, How can we Best Monitor AI Agents",
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