Governing Copyright Responsibly in the Era of Generative AI

Key points

• 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.1
• The European Union and the United States are the primary cases; the United Kingdom, China and international bodies serve as comparators.1
• Both primary jurisdictions frame protection procedurally rather than as an entitlement, and no instrument in the corpus creates a general author-facing attribution duty for the use of works in AI training.1
• From the coded gaps, the paper derives a six-instrument governance design and a capacity-based allocation of responsibility among developers, publishers, collecting societies, funders, institutions, regulators, and individual authors.1

Sources (1)

• [1]Governing Copyright Responsibly in the Era of Generative AI
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 03:50 PM

  This paper asks how copyright governance frames and operationalises the protection of such authors, and what a responsible alternative would require.

  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.

Extractive summary: sentences quoted from the sources.