Guest Post — Research Needs a Shared Standard for AI Disclosure
Today’s guest post presents outcomes of the WCRI’s (World Conferences on Research Integrity) Focus Track to address this gap in clarity and standardization of AI disclosure.
Today’s guest post presents outcomes of the WCRI’s (World Conferences on Research Integrity) Focus Track to address this gap in clarity and standardization of AI disclosure.
Today’s post asks us to acknowledge the role of AI in peer review and ensure practical guidance and policies that help scholars respond with consistency and confidence.
The future of scholarly communication will not be determined by how powerful AI becomes, but by whether the research community remains clear about the purpose those capabilities are meant to serve and whether it can govern them together.
A Cambridge workshop proposes new standard work to support provenance, attribution and metrics in scholarly communications AI tools.
While it’s true that AI may be viewed as “legitimate,” it’s far from universally loved. Understanding that distinction tells us something important about how the technology may ultimately be adopted — and governed — within academia.
Today’s post explores what happens to the scholarly content platform when AI agents become the users.
Today, we offer reflections on the SSP Annual Meeting from our 2026 Fellowship cohort.
Building robust citation and attribution into generative AI systems are foundational to usage, credit and trust. We need to expect more from AI.
A German court ruled against a mandatory article deposit requirement under Germany’s “secondary publication right” (SPR). Whatever the intentions, SPR is mainly going to contribute to the degradation of the record of science.
We asked some of the attendees of the recent SSP Annual Meeting in Chula Vista, CA, to answer the question: “What are some takeaways from your experience at SSP 2026?”
A conversation on AI retrieval, the provenance problem, and the shared infrastructure scholarly publishing needs.
Today’s guest post asserts that AI infrastructure will let publishers truly leverage machines, while brand and community are what will keep them meaningful to humans.
AI scholarly search tools often miss important literature due to incomplete metadata. Better full-text-derived metadata could significantly improve discovery.
For scholarly publishers, the user has changed faster than the systems designed to serve them, and the gap between the two is where most of the difficult work is happening.
With CC Signals, Creative Commons wants to help authors put rules on use of their licensed content for AI training. The problem is, one of the licenses already permits free and unlimited reuse of that content, for any and all purposes. And the licenses are irrevocable.