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 "name": "Streaming Legal Logic Of Ai Data Governance Based On Federated Learning Institutio 002B314Ca7",
 "text": "Legal Logic of AI Data Governance Based on Federated Learning: Institutional Evolution from Privacy Protection to Rights Distribution presents the following source proposition: Through legal reasoning and literature analysis, it delves into the importance of federated learning as a key institutional approach that balances privacy and data utilization in the face of real-world challenges such as conflicts between data silos and privacy protection, disputes over the ownership of data rights in AI training works, and compliance pressures from the EU AI Act and GDPR. This proposition is pertinent to Kaal's source-bound claim that Managing machine learning assets and complying with laws such as GDPR and CCPA becomes significantly harder under decentralized governance, because distributed data and operations complicate tracking data flows, enforcing privacy controls, and demonstrating compliance during audits. 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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  "The source-to-claim mapping remains explicitly ambiguous and is published with that limitation."
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  "url": "https://doi.org/10.64229/efs37007"
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  "paper": "Wulf A. Kaal, AI Governance",
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