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 "identifier": "kaal:entity:dynamic-feedback",
 "name": "Dynamic feedback",
 "termCode": "dynamic-feedback",
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 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0000-0003-0757-275X"
 },
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   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/4796714-024",
   "identifier": "kaal:claim:4796714-024",
   "text": "Ex-ante regulation is preferable in principle but is neither practical nor sufficient inside legacy systems, precisely because legacy systems, unlike the web3 system proposed here, are not equipped to create dynamic feedback effects.",
   "abstract": "while ex ante regulation is preferable, it is not always practical or sufficient in legacy ssytems to address the dynamic challenges that AI technologies present because legacy systems, unlike web3 systems as proposed herein, are not typically equipped to create dynamic feedback effects.",
   "citation": "Wulf A. Kaal, AI Governance (2024). SSRN: https://ssrn.com/abstract=4796714",
   "datePublished": "2024",
   "claim_type": "condition",
   "confidence": "argued",
   "is_failure_mode": true,
   "scope_conditions": [
    "comparison is between legacy regulatory infrastructure and web3 based governance",
    "turns on the presence or absence of dynamic feedback effects"
   ],
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   "status": "current"
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  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/4941807-005",
   "identifier": "kaal:claim:4941807-005",
   "text": "Integrating feedback directly into governance processes allows stakeholders to iteratively adjust AI models as new information, operational experience, and changed environments arrive, which mitigates risks and biases more effectively than static ex-post regulatory frameworks.",
   "abstract": "stakeholders can iteratively improve and adjust AI models in response to new information, operational experiences, and changing environments. This ongoing process helps mitigate risks and biases more effectively than static, ex-post regulatory frameworks.",
   "citation": "Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024). SSRN: https://ssrn.com/abstract=4941807",
   "datePublished": "2024",
   "claim_type": "mechanism",
   "confidence": "argued",
   "is_failure_mode": false,
   "scope_conditions": [
    "where feedback is embedded in the governance process itself",
    "where models can still be adjusted after deployment"
   ],
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   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/4941807-036",
   "identifier": "kaal:claim:4941807-036",
   "text": "Unlike traditional governance models that depend on periodic reviews and updates, the proposed WDAG system draws on a continuous stream of data from a wide range of stakeholders, which is what makes its ethical frameworks track current societal values in real time.",
   "abstract": "Unlike traditional governance models that rely on periodic reviews and updates, WDAG can tap into a continuous stream of data from a wide range of stakeholders.",
   "citation": "Wulf A. Kaal, AI Governance Via Web3 Reputation System (2024). SSRN: https://ssrn.com/abstract=4941807",
   "datePublished": "2024",
   "claim_type": "mechanism",
   "confidence": "argued",
   "is_failure_mode": false,
   "scope_conditions": [
    "where a decentralized network continuously supplies stakeholder sentiment data"
   ],
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 "description": "3 claims in the published works of Wulf A. Kaal carry the concept tag 'dynamic-feedback'. Derived node: a roster, not an adjudicated definition."
}