{
 "@context": "https://schema.org",
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 "@id": "https://wulfkaal.github.io/positions/2026-07-31-7626",
 "identifier": "kaal:position:2026-07-31-7626",
 "additionalType": "https://wulfkaal.github.io/positions/schema.json#AffirmedPositionClaim",
 "name": "Streaming Explainable Ai Models For Ethical Marketing C3201F6223",
 "text": "Explainable AI Models for Ethical Marketing presents the following source proposition: To be transparent and interpretable to the regulators, the system integrates global and local explanations based on SHAP with the generation of counterfactual. This proposition is pertinent to Kaal's source-bound claim that Explainable reinforcement learning research has not yet produced usable explanations: the field relies on toy examples, omits user testing, produces explanations that are themselves complex, uses basic visualizations, and rarely open sources its code. 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.",
 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0009-0008-7840-1847"
 },
 "datePublished": "2026-07-31",
 "dateModified": "2026-07-31",
 "creativeWorkStatus": "Affirmed",
 "responseType": "extension",
 "keywords": [
  "ai-and-agents",
  "historical-response",
  "scholarly-literature",
  "crossref"
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 "scope_conditions": [
  "The response is limited to the retrieved source proposition and mapped Kaal claim unless fuller source review supports a broader conclusion.",
  "External evidence level: abstract indexed.",
  "Mapping review tier: moderate-confidence claim review.",
  "Primary mapping confidence: 0.4047.",
  "The source-to-claim mapping remains explicitly ambiguous and is published with that limitation."
 ],
 "currentDebate": {
  "name": "Explainable AI Models for Ethical Marketing",
  "url": "https://doi.org/10.4018/979-8-2600-0532-3.ch014"
 },
 "extends": {
  "identifier": "kaal:claim:4855607-013",
  "url": "https://wulfkaal.github.io/claims/4855607-013",
  "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": [
   "Wulf A. Kaal"
  ],
  "year": "2024",
  "ssrn": "https://ssrn.com/abstract=4855607",
  "source_pdf_sha256": "eb0b3e62374b45a8fa888c6bde9725e606bcb46cf4b5e74a6e851d9f25099113"
 },
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   "@id": "https://wulfkaal.github.io/claims/4855607-013"
  },
  {
   "@type": "CreativeWork",
   "name": "Explainable AI Models for Ethical Marketing",
   "url": "https://doi.org/10.4018/979-8-2600-0532-3.ch014"
  }
 ],
 "batch_id": "kaal-review:2026-07-31:streaming-etl-0009",
 "review_provenance": "https://kaal-signal-desk.wulf577462.chatgpt.site/#review",
 "publicationStatus": "public",
 "recordTypeNote": "Dated commentary position extending a scholarly corpus claim. Not a verbatim claim extracted from the paper.",
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 "canonical_url": "https://wulfkaal.github.io/positions/2026-07-31-7626",
 "canonicalForm": "https://wulfkaal.github.io/positions/2026-07-31-7626.md",
 "candidateId": "kaal:response-draft:2026-07-31:32a9afe47fc09fa225cd",
 "evidenceLevel": "abstract indexed",
 "reviewTier": "moderate-confidence claim review",
 "mappingConfidence": 0.4047,
 "mappingAmbiguous": true,
 "mappingMethod": "idf-weighted multi-field mapping v1",
 "mappingWhyRelevant": "Shared high-information concepts: explainable, transparent, interpretable, explanations. Scope: current explainable RL research as surveyed in the text.",
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  "source": "crossref",
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  "queryId": "concept:b5ce5d92d4b5",
  "queryText": "AI agent governance",
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  "doi": "10.4018/979-8-2600-0532-3.ch014",
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  },
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  "workId": "work:doi:10.4018/979-8-2600-0532-3.ch014",
  "workAuthors": [
   "Shubhendu Shekher Shukla",
   "K. Kishore Kumar",
   "D. Bhavana",
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    "claimUrl": "https://wulfkaal.github.io/claims/4855607-013",
    "rank": 1,
    "confidence": 0.4047,
    "method": "idf-weighted multi-field mapping v1",
    "whyRelevant": "Shared high-information concepts: explainable, transparent, interpretable, explanations. Scope: current explainable RL research as surveyed in the text.",
    "ambiguous": true
   },
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    "method": "idf-weighted multi-field mapping v1",
    "whyRelevant": "Shared high-information concepts: explainable, transparent, explanations. Scope: systems using computational models of argument to generate legally grounded explanations.",
    "ambiguous": true
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 "userAffirmation": "I affirm batch kaal-review:2026-07-31:streaming-etl-0009, SHA-256 b79e655db33b69d4e6de8a205792c1e02d068eea4880d69cd6faeb884041abdf, as written and authorize publication of all 250 response claims on my canonical property, preserving their evidence levels, ambiguity labels, provenance, and the unchanged 5,033 scholarly claims.",
 "sha256": "236e9d178cd62b1eb31553829bb3c545df88d28ec7143aead0f1dc2096f31917"
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