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AI Copyright Governance: EU, US Lead Procedural Frameworks, Lacking Author Attribution

By PulseAugur Editorial · [1 sources] ·

A new paper analyzes copyright governance concerning generative AI, examining 40 legal and soft-law instruments from 2019-2026. The research focuses on the European Union and the United States, with comparisons to the United Kingdom and China, finding that most instruments frame author protection procedurally rather than as an entitlement. The paper highlights a lack of general author-facing attribution duties for AI training data and proposes a six-instrument governance design to address these gaps. AI

IMPACT This research highlights potential gaps in author rights and attribution for AI training data, suggesting a need for revised governance frameworks.

RANK_REASON The cluster contains an academic paper analyzing policy and legal frameworks related to AI. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI Copyright Governance: EU, US Lead Procedural Frameworks, Lacking Author Attribution

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19 / 100
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The cluster contains an academic paper analyzing policy and legal frameworks related to AI. [lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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policy, paper
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High
Clearly on-topic for AI-industry coverage.
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Breaking (< 6h)
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · D. Kochetkov ·

    Governing Copyright Responsibly in the Era of Generative AI

    arXiv:2610.17002v1 Announce Type: cross Abstract: Large language models are trained on collections that may include scholarly works, while authors generally lack work-level information about whether and how their works were used. This paper asks how copyright governance frames an…

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