Recently, a group of Ukrainian researchers uncovered serious violations in the use of ISSN identifiers by journals operating in temporarily occupied territories, revealing systematic misuse of academic infrastructure and promoting narratives hostile to Ukraine.
The first AI training case has been decided in the US in favor of the copyright holder.
“Rights reservation language, whether in plain English, included in terms, or coded into, e.g., metadata, is “machine readable.” It is a choice by an AI developer to not read “human readable” rights reservation language.”
In copyright law, the existence of licensing options impacts upon a rights owners exclusive rights.
Legislation often lags technological advances. The EU’s Digital Single Market Copyright Directive leaves many open questions regarding AI text- and data-mining.
Before we launch into 2024, a look back at 2023 in The Scholarly Kitchen.
Introducing the AI in Scholarly Publishing Community of Interest (CoIN), the SSP’s latest offering to all its members to explore and engage in all matters AI as they relate to scholarly publishing.
We asked the Chefs for their thoughts on the Biden Administration’s Executive Order on “Safe, Secure, and Trustworthy Artificial Intelligence.”
A selection of questions and answers from Copyright Clearance Center’s response to the United States Copyright Office “Artificial Intelligence and Copyright” request for comment.
A report of the Chef’s panel on AI, Open content, and research integrity during the Frankfurt Book Fair.
The role of libraries and archives as streaming grows, choice declines, and the death of the red envelopes arrives.
An appeals court has ruled that it is unconstitutional for the government to require deposit of published works in the Library of Congress
Are scholarly publishers primed to become the critical content suppliers for the big Generative AI companies?
The AI takeover isn’t all doom and gloom. Finally, a long running musical question can be answered.
The current uproar over artificial intelligence does not show us what the future of AI will look like, but rather how a human population falls into predictable patterns as it contemplates any new development: we are observing not AI but ourselves observing AI.