AI copyright report: training data, courts and licensing
Daily evidence on AI copyright, training data, lawsuits, licensing and creator rights, linked to original reporting and research. Coverage counts are signals of attention—not measures of importance, harm or consensus.
Prepared by the ethics.ai evidence desk · automatically refreshed · editorial scope reviewed against the methodology and corrections policy
Tracks litigation, policy, licensing, provenance and creator-rights disputes involving generative AI. It is not legal advice and does not infer a case outcome from coverage volume.
Questions to take into the evidence
Which legal theories are being tested in court?
How are licensing and consent models changing?
What technical provenance measures are being proposed?
AI-generated books make up 20 percent of Amazon's self-published catalog but bring in only 12 percent of sales. A new study finds that revenue per book is dropping for human-written titles too, in seven of eight genres. The findings could give copyright plaintiffs the market-harm data their cases against AI companies have been missing. The article AI-generated books are flooding Amazon and tanking sales for human authors appeared first on The Decoder .
The Delhi High Court questioned Meta over alleged misuse of its Rights Manager tool, after creators said scammers used fraudulent copyright claims to remove original content and target accounts. The post How Meta’s copyright management tools are being exploited; Delhi HC to examine appeared first on MEDIANAMA .
BMG is the first significant rightsholder to ink a licensing deal with Suno since Warner Music Group announced an alliance with the AI company nine months ago. Source
Graph Neural Networks (GNNs) serve as the backbone for high-stakes applications in Machine-Learning-as-a-Service (MLaaS). Still, their black-box deployment exposes them to Model Extraction (ME) attacks, in which adversaries steal intellectual property by querying APIs. Existing defenses suffer from a critical ''Euclidean bias'': they transfer image-based strategies (e.g., random noise) to graphs, ignoring the complex topological dependencies between nodes, which often results in severe utility d
GMR filed the complaint on June 8, alleging that Music Choice continued performing songs from its catalog after the companies' license agreement lapsed. Source
Leading large language model providers now conceal their models' step-by-step reasoning, or chain-of-thought, to protect intellectual property and limit information leakage. Rather than storing these traces server-side, providers return them to the client as blocks of encrypted text, which the client passes back with each subsequent request. Building on prior research, we identify an architectural vulnerability: these encrypted blocks are fully compatible and interchangeable across different ses
This report is assembled automatically from source metadata and keyword classifications. It summarizes what the tracked source fleet published; it does not independently validate every linked claim. Source-fleet growth can inflate historical comparisons. Cite the individual evidence record and original publisher for substantive claims.
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