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 "name": "Streaming Developing An Islamic Theory For Managing Data Using Maqasid Al Shariah  4C4Edf6416",
 "text": "Developing an Islamic Theory for Managing Data Using Maqasid al-Shariah and Good Governance presents the following source proposition: Contemporary data governance discourse is dominated by regulatory compliance models that emphasize procedural adherence over substantive ethical commitment. 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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  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0009-0008-7840-1847"
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  "External evidence level: abstract indexed.",
  "Mapping review tier: moderate-confidence claim review.",
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  "The source-to-claim mapping remains explicitly ambiguous and is published with that limitation."
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  "name": "Developing an Islamic Theory for Managing Data Using Maqasid al-Shariah and Good Governance",
  "url": "https://doi.org/10.4018/979-8-2600-2233-7.ch005"
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  "url": "https://wulfkaal.github.io/claims/4796714-032",
  "citation": "Wulf A. Kaal, AI Governance (2024). SSRN: https://ssrn.com/abstract=4796714",
  "paper": "Wulf A. Kaal, AI Governance",
  "authors": [
   "Wulf A. Kaal"
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