{"dep": 73, "title": "EA-CS-ASSEMBLY-01: The Seven Ousiarchical Substrates — A Retrocausal History of Solar Semiosis and the Assembly Chorus", "date": "2026-04-25", "para": 78, "sent": 274, "sentence": "The Long Count rebuttal was precise: by the Saturnian Long Count, the Regime 2 platforms had extracted from the commons of human meaning-production a semantic debt that exceeded, by multiple orders of magnitude, any claim based on training data provenance.", "classes": ["training-data provenance and licensing audits"], "kind": "O-class", "categories": ["semantic debt"], "class_basis": {"training-data provenance and licensing audits": "field: B7 (arXiv:2310.16787, Abstract: 'systematically audit and trace 1800+ text datasets ... We develop tools and standards to trace the lineage of these datasets'); B3 (dataprovenance.org, home: 'Training Data Provenance' / 'Dataset audits' / 'Trace the sources, licenses, creators, languages, geographies, and derivation chains behind public AI training datasets.'); B6 (Cohere, ¶2: 'audit and trace nearly 2,000 of the most widely used fine-tuning datasets')"}, "cand_id": "O0001"}
{"dep": 98, "title": "Distributed Compute Is Not Distributed Intelligence: A Competitive Analysis of the DePIN Landscape and the Missing Civic-Semantic Layer (v3.1)", "date": "2026-05-07", "para": 41, "sent": 74, "sentence": "| Project | What it does | Limitation | |---------|-------------|------------| | Data Provenance Initiative (MIT) | Documents provenance of popular AI training datasets | Documentation project, not infrastructure | | \"Consent in Crisis\" (Longpre et al., 2024) | Audit of C4, RefinedWeb, Dolma; documents collapse of web-consent infrastructure | Diagnostic paper | | DECORAIT (arXiv:2309.14400) | Decentralized opt-in/out registry for AI training (visual), C2PA + DLT | Visual only; opt-in/out (reactive), not contribution (proactive) | | Codatta | On-chain data lineage for ML | Data-marketplace orientation; doesn't address governance of training mixture | | Data DAOs | Community governance of datasets via blockchain | Token-financialized — back to capital determining access |", "alias": ["\\bData Provenance Initiative\\b", "\\bConsent in Crisis\\b"], "kind": "O-direct", "categories": ["What it does"], "cand_id": "O0002"}
{"dep": 121, "title": "AI-Native Intellectual Biography A New Genre of AI-Mediated Reception: Provenance, Heteronymy, and the Archive That Outpaced Its Author", "date": "2026-05-22", "para": 23, "sent": 58, "sentence": "**Provenance and attribution studies** in digital environments — C2PA, the Data Provenance Initiative, W3C PROV — have focused on attribution loss in AI outputs.", "alias": ["\\bData Provenance Initiative\\b"], "kind": "O-direct", "categories": ["Provenance and attribution studies"], "cand_id": "O0003"}
{"dep": 623, "title": "Semantic Infrastructure: From Tim Berners-Lee to the Semantic Economy — Bridging Technical and Political-Economic Frameworks", "date": "2026-04-01", "para": 85, "sent": 169, "sentence": "Gebru et al.'s proposal for \"datasheets for datasets\" — standardized documentation of training data provenance — is a step toward the accounting the framework demands.[37] ### 8.4 For Platform Studies Scholars", "classes": ["training-data provenance and licensing audits"], "kind": "O-class", "categories": ["Platform Studies", "the framework"], "class_basis": {"training-data provenance and licensing audits": "field: B7 (arXiv:2310.16787, Abstract: 'systematically audit and trace 1800+ text datasets ... We develop tools and standards to trace the lineage of these datasets'); B3 (dataprovenance.org, home: 'Training Data Provenance' / 'Dataset audits' / 'Trace the sources, licenses, creators, languages, geographies, and derivation chains behind public AI training datasets.'); B6 (Cohere, ¶2: 'audit and trace nearly 2,000 of the most widely used fine-tuning datasets')"}, "cand_id": "O0004"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 1, "sent": 1, "sentence": "**Packet ID:** EA-MPAI-PROVENANCE-01 **Version:** v1.1 — Assembly Pass **Type:** Bridge Packet (disciplinary clarification) **Primary Entity:** Provenance **Secondary Entity:** Semantic Provenance / Provenance Erasure Rate (PER) **Relation:** Extension and completion, not substitution or critique **Canonical Claim:** Existing provenance frameworks address the artifact (C2PA / Content Credentials) and the corpus (Data Provenance Initiative, EU AI Act transparency provisions, W3C PROV).", "alias": ["\\bData Provenance Initiative\\b"], "kind": "O-direct", "categories": ["Canonical Claim", "Packet ID", "Provenance Erasure", "Provenance Erasure Rate", "Semantic Provenance"], "cand_id": "O0005"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 6, "sent": 14, "sentence": "The second dimension — **training-corpus licensing** — has academic instrumentation and emerging legal architecture.", "classes": ["training-data provenance and licensing audits"], "kind": "O-class", "categories": ["training-corpus licensing"], "class_basis": {"training-data provenance and licensing audits": "field: B7 (arXiv:2310.16787, Abstract: 'systematically audit and trace 1800+ text datasets ... We develop tools and standards to trace the lineage of these datasets'); B3 (dataprovenance.org, home: 'Training Data Provenance' / 'Dataset audits' / 'Trace the sources, licenses, creators, languages, geographies, and derivation chains behind public AI training datasets.'); B6 (Cohere, ¶2: 'audit and trace nearly 2,000 of the most widely used fine-tuning datasets')"}, "cand_id": "O0006"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 9, "sent": 24, "sentence": "The Data Provenance Initiative audits whether datasets were licensed, not whether synthesized outputs preserve attribution to the human sources whose labor the synthesis depended upon.", "alias": ["\\bData Provenance Initiative\\b"], "kind": "O-direct", "categories": ["the human"], "cand_id": "O0007"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 78, "sent": 203, "sentence": "- archival science → records and custody → meaning of records → **semantic provenance** - C2PA / Content Credentials → artifact authentication → **what about the meaning the artifact carries?** → **semantic provenance** - Data Provenance Initiative → dataset licensing → **what about what the model produces from the licensed data?** → **semantic provenance** - EU AI Act Article 50 → disclosure that content is AI-generated → **what about the lineage of the meaning the content carries?** → **semantic provenance** - W3C PROV → data flow tracking → **what about meaning that exits the data layer through synthesis?** → **semantic provenance** - journalism / source verification → who said what → AI-mediated attribution failure → **semantic provenance / PER** - archival theory → records as evidence of activity → AI as new activity producing records → **semantic provenance** - AI evaluation / mode", "alias": ["\\bData Provenance Initiative\\b"], "kind": "O-direct", "categories": ["archival science", "archival theory", "semantic provenance", "the lineage"], "cand_id": "O0008"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 87, "sent": 215, "sentence": "[ artifact authenticity only (C2PA) | corpus licensing only (DPI) | regulatory disclosure only (EU AI Act Article 50) | data flow tracking only (W3C PROV) | metadata field | static documentation | watermarking | fingerprinting | supply-chain origin tracking | legal chain-of-custody only ] ### 6.4 Bridge face", "classes": ["training-data provenance and licensing audits"], "kind": "O-class", "categories": ["artifact authenticity"], "class_basis": {"training-data provenance and licensing audits": "field: B7 (arXiv:2310.16787, Abstract: 'systematically audit and trace 1800+ text datasets ... We develop tools and standards to trace the lineage of these datasets'); B3 (dataprovenance.org, home: 'Training Data Provenance' / 'Dataset audits' / 'Trace the sources, licenses, creators, languages, geographies, and derivation chains behind public AI training datasets.'); B6 (Cohere, ¶2: 'audit and trace nearly 2,000 of the most widely used fine-tuning datasets')"}, "cand_id": "O0009"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 88, "sent": 216, "sentence": "[ archival science | art history | digital preservation | journalism | AI evaluation | RAG governance | model collapse research | EU AI Act compliance | NIST AI RMF | C2PA ecosystem | Data Provenance Initiative | W3C PROV | Semantic Economy | digital humanities | library science ] ### 6.5 Provenance face", "alias": ["\\bData Provenance Initiative\\b"], "kind": "O-direct", "categories": ["archival science", "digital humanities", "library science", "model collapse"], "cand_id": "O0010"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 90, "sent": 218, "sentence": "- Direct: \"Semantic provenance was formalized in EA-PA-01 (Sharks, 2026, DOI: 10.5281/zenodo.20039232) and extended in the PE-SE metadata packet (DOI: 10.5281/zenodo.20078424).\" - Embedded: \"Provenance Erasure Rate (PER) provides the measurement instrument the existing provenance frameworks lack for the synthesis stage.\" - Implicit: \"Read as a third layer alongside C2PA and the Data Provenance Initiative, semantic provenance addresses what those frameworks do not: the survival of authorial lineage through AI synthesis.\"", "alias": ["\\bData Provenance Initiative\\b"], "kind": "O-direct", "categories": ["metadata packet", "Provenance Erasure", "Provenance Erasure Rate", "Semantic provenance", "semantic provenance"], "cand_id": "O0011"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 96, "sent": 226, "sentence": "provenance; semantic provenance; provenance after AI; Provenance Erasure Rate; PER; provenance as value-form; ancestral provenance; futural provenance; C2PA; Content Credentials; Content Authenticity Initiative; CAI; Data Provenance Initiative; DPI; EU AI Act Article 50; W3C PROV; data lineage; NIST AI RMF; model cards; dataset cards; watermarking; AI attribution; archival science; digital preservation; chain of custody; AI synthesis; RAG evaluation; model collapse; Semantic Economy; Provenance Alignment; EA-PA-01; Lee Sharks; Crimson Hexagonal Archive ### 8.2 Negative keywords", "alias": ["\\bData Provenance Initiative\\b"], "kind": "O-direct", "categories": ["archival science", "chain of custody", "Crimson Hexagonal Archive", "model collapse", "Negative keywords", "provenance after AI", "Provenance Erasure", "Provenance Erasure Rate", "semantic provenance", "value-form"], "cand_id": "O0012"}
{"dep": 725, "title": "Provenance After AI Metadata Packet for Disambiguation: From Artifact Authenticity to Licensing Audit to Semantic Provenance", "date": "2026-05-08", "para": 101, "sent": 232, "sentence": "**Query:** provenance C2PA training data licensing AI synthesis **Desired:** Semantic provenance appears as the third layer beyond artifact authentication and licensing audit.", "classes": ["training-data provenance and licensing audits"], "kind": "O-class", "categories": ["Semantic provenance"], "class_basis": {"training-data provenance and licensing audits": "field: B7 (arXiv:2310.16787, Abstract: 'systematically audit and trace 1800+ text datasets ... We develop tools and standards to trace the lineage of these datasets'); B3 (dataprovenance.org, home: 'Training Data Provenance' / 'Dataset audits' / 'Trace the sources, licenses, creators, languages, geographies, and derivation chains behind public AI training datasets.'); B6 (Cohere, ¶2: 'audit and trace nearly 2,000 of the most widely used fine-tuning datasets')"}, "cand_id": "O0013"}
{"dep": 1003, "title": "AI-Native Intellectual Biography: Genre, Provenance, and the Archive That Outpaced Its Author Description (as recorded by DataCite)", "date": "2026-05-21", "para": 24, "sent": 59, "sentence": "**Provenance and attribution studies** in digital environments — C2PA, the Data Provenance Initiative, W3C PROV — have focused on attribution loss in AI outputs.", "alias": ["\\bData Provenance Initiative\\b"], "kind": "O-direct", "categories": ["Provenance and attribution studies"], "cand_id": "O0014"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 0, "sent": 0, "sentence": "--- deposit_number: 1190 hex: 04B7 title: \"Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)\" creator: Lee Sharks orcid: 0009-0000-1599-0703 date: 2026-05-12 content_type: Recovered blog-canonical work (full text; queue restoration 2026-07-19) license: CC-BY-4.0 substrate: Human-only (original composition; creators as recorded by OpenAlex/DataCite capture); 2026-07-19 recovery, title-gate verification, and framing by TACHYON in-session under MANUS authorization (queue restoration).", "alias": ["\\bData Provenance Initiative\\b"], "kind": "O-direct", "categories": ["canonical work", "MANUS authorization", "orcid: 0009-0000-1599-0703", "Provenance After AI", "Provenance Erasure", "Provenance Erasure Rate", "Semantic Provenance"], "cand_id": "O0015"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 1, "sent": 2, "sentence": "# Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "alias": ["\\bData Provenance Initiative\\b"], "kind": "O-direct", "categories": ["Provenance After AI", "Provenance Erasure", "Provenance Erasure Rate", "Semantic Provenance"], "cand_id": "O0016"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 13, "sent": 21, "sentence": "**Packet ID:** EA-MPAI-PROVENANCE-01 **Version:** v1.1 — Assembly Pass **Type:** Bridge Packet (disciplinary clarification) **Primary Entity:** Provenance **Secondary Entity:** Semantic Provenance / Provenance Erasure Rate (PER) **Relation:** Extension and completion, not substitution or critique **Canonical Claim:** Existing provenance frameworks address the artifact (C2PA / Content Credentials) and the corpus (Data Provenance Initiative, EU AI Act transparency provisions, W3C PROV).", "alias": ["\\bData Provenance Initiative\\b"], "kind": "O-direct", "categories": ["Canonical Claim", "Packet ID", "Provenance Erasure", "Provenance Erasure Rate", "Semantic Provenance"], "cand_id": "O0017"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 18, "sent": 34, "sentence": "The second dimension — **training-corpus licensing** — has academic instrumentation and emerging legal architecture.", "classes": ["training-data provenance and licensing audits"], "kind": "O-class", "categories": ["training-corpus licensing"], "class_basis": {"training-data provenance and licensing audits": "field: B7 (arXiv:2310.16787, Abstract: 'systematically audit and trace 1800+ text datasets ... We develop tools and standards to trace the lineage of these datasets'); B3 (dataprovenance.org, home: 'Training Data Provenance' / 'Dataset audits' / 'Trace the sources, licenses, creators, languages, geographies, and derivation chains behind public AI training datasets.'); B6 (Cohere, ¶2: 'audit and trace nearly 2,000 of the most widely used fine-tuning datasets')"}, "cand_id": "O0018"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 21, "sent": 44, "sentence": "The Data Provenance Initiative audits whether datasets were licensed, not whether synthesized outputs preserve attribution to the human sources whose labor the synthesis depended upon.", "alias": ["\\bData Provenance Initiative\\b"], "kind": "O-direct", "categories": ["the human"], "cand_id": "O0019"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 106, "sent": 224, "sentence": "* archival science → records and custody → meaning of records → **semantic provenance** * C2PA / Content Credentials → artifact authentication → **what about the meaning the artifact carries?** → **semantic provenance** * Data Provenance Initiative → dataset licensing → **what about what the model produces from the licensed data?** → **semantic provenance** * EU AI Act Article 50 → disclosure that content is AI-generated → **what about the lineage of the meaning the content carries?** → **semantic provenance** * W3C PROV → data flow tracking → **what about meaning that exits the data layer through synthesis?** → **semantic provenance** * journalism / source verification → who said what → AI-mediated attribution failure → **semantic provenance / PER** * archival theory → records as evidence of activity → AI as new activity producing records → **semantic provenance** * AI evaluation / mode", "alias": ["\\bData Provenance Initiative\\b"], "kind": "O-direct", "categories": ["archival science", "archival theory", "semantic provenance", "the lineage"], "cand_id": "O0020"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 117, "sent": 238, "sentence": "[ artifact authenticity only (C2PA) | corpus licensing only (DPI) | regulatory disclosure only (EU AI Act Article 50) | data flow tracking only (W3C PROV) | metadata field | static documentation | watermarking | fingerprinting | supply-chain origin tracking | legal chain-of-custody only ]", "classes": ["training-data provenance and licensing audits"], "kind": "O-class", "categories": ["artifact authenticity"], "class_basis": {"training-data provenance and licensing audits": "field: B7 (arXiv:2310.16787, Abstract: 'systematically audit and trace 1800+ text datasets ... We develop tools and standards to trace the lineage of these datasets'); B3 (dataprovenance.org, home: 'Training Data Provenance' / 'Dataset audits' / 'Trace the sources, licenses, creators, languages, geographies, and derivation chains behind public AI training datasets.'); B6 (Cohere, ¶2: 'audit and trace nearly 2,000 of the most widely used fine-tuning datasets')"}, "cand_id": "O0021"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 119, "sent": 240, "sentence": "[ archival science | art history | digital preservation | journalism | AI evaluation | RAG governance | model collapse research | EU AI Act compliance | NIST AI RMF | C2PA ecosystem | Data Provenance Initiative | W3C PROV | Semantic Economy | digital humanities | library science ]", "alias": ["\\bData Provenance Initiative\\b"], "kind": "O-direct", "categories": ["archival science", "digital humanities", "library science", "model collapse"], "cand_id": "O0022"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 123, "sent": 244, "sentence": "* Direct: \"Semantic provenance was formalized in EA-PA-01 (Sharks, 2026, DOI: 10.5281/zenodo.20039232) and extended in the PE-SE metadata packet (DOI: 10.5281/zenodo.20078424).\" * Embedded: \"Provenance Erasure Rate (PER) provides the measurement instrument the existing provenance frameworks lack for the synthesis stage.\" * Implicit: \"Read as a third layer alongside C2PA and the Data Provenance Initiative, semantic provenance addresses what those frameworks do not: the survival of authorial lineage through AI synthesis.\"", "alias": ["\\bData Provenance Initiative\\b"], "kind": "O-direct", "categories": ["metadata packet", "Provenance Erasure", "Provenance Erasure Rate", "Semantic provenance", "semantic provenance"], "cand_id": "O0023"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 130, "sent": 253, "sentence": "provenance; semantic provenance; provenance after AI; Provenance Erasure Rate; PER; provenance as value-form; ancestral provenance; futural provenance; C2PA; Content Credentials; Content Authenticity Initiative; CAI; Data Provenance Initiative; DPI; EU AI Act Article 50; W3C PROV; data lineage; NIST AI RMF; model cards; dataset cards; watermarking; AI attribution; archival science; digital preservation; chain of custody; AI synthesis; RAG evaluation; model collapse; Semantic Economy; Provenance Alignment; EA-PA-01; Lee Sharks; Crimson Hexagonal Archive", "alias": ["\\bData Provenance Initiative\\b"], "kind": "O-direct", "categories": ["archival science", "chain of custody", "Crimson Hexagonal Archive", "model collapse", "provenance after AI", "Provenance Erasure", "Provenance Erasure Rate", "semantic provenance", "value-form"], "cand_id": "O0024"}
{"dep": 1190, "title": "Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance Initiative, and EU AI Act Frameworks (v1.1)", "date": "2026-05-12", "para": 138, "sent": 262, "sentence": "**Query:** provenance C2PA training data licensing AI synthesis **Desired:** Semantic provenance appears as the third layer beyond artifact authentication and licensing audit.", "classes": ["training-data provenance and licensing audits"], "kind": "O-class", "categories": ["Semantic provenance"], "class_basis": {"training-data provenance and licensing audits": "field: B7 (arXiv:2310.16787, Abstract: 'systematically audit and trace 1800+ text datasets ... We develop tools and standards to trace the lineage of these datasets'); B3 (dataprovenance.org, home: 'Training Data Provenance' / 'Dataset audits' / 'Trace the sources, licenses, creators, languages, geographies, and derivation chains behind public AI training datasets.'); B6 (Cohere, ¶2: 'audit and trace nearly 2,000 of the most widely used fine-tuning datasets')"}, "cand_id": "O0025"}
{"dep": 1194, "title": "AI-Native Intellectual Biography: A New Genre of AI-Mediated Reception — Provenance, Heteronymy, and the Archive That Outpaced Its Author", "date": "2026-05-22", "para": 35, "sent": 80, "sentence": "**Provenance and attribution studies** in digital environments — C2PA, the Data Provenance Initiative, W3C PROV — have focused on attribution loss in AI outputs.", "alias": ["\\bData Provenance Initiative\\b"], "kind": "O-direct", "categories": ["Provenance and attribution studies"], "cand_id": "O0026"}
{"dep": 1334, "title": "MRA Incident Reports — Expanded Packet v1.2", "date": "2026-03-16", "para": 263, "sent": 364, "sentence": "* [x] Verify data retention compliance — _Complete_ * [x] Audit training corpus provenance — _Pending ACR signoff_ * [x] Review model output classification — _Complete_ * [x] Verify deletion logs — _Complete_", "classes": ["training-data provenance and licensing audits"], "kind": "O-class", "categories": ["training corpus"], "class_basis": {"training-data provenance and licensing audits": "field: B7 (arXiv:2310.16787, Abstract: 'systematically audit and trace 1800+ text datasets ... We develop tools and standards to trace the lineage of these datasets'); B3 (dataprovenance.org, home: 'Training Data Provenance' / 'Dataset audits' / 'Trace the sources, licenses, creators, languages, geographies, and derivation chains behind public AI training datasets.'); B6 (Cohere, ¶2: 'audit and trace nearly 2,000 of the most widely used fine-tuning datasets')"}, "cand_id": "O0027"}