{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Margin of Thought with Priten","title":"What's the Line Between Research Integrity and Using AI as a Tool? - Kari Weaver","html":"","width":"100%","height":180,"duration":2309,"description":"In this episode, Priten speaks with Kari Weaver, a librarian educator and program manager for the Artificial Intelligence and Machine Learning Initiative at the Ontario Council of University Libraries (OCUL), about why existing tools like citation and methodology sections can't capture how AI is actually being used in research and learning -- and what a structured disclosure standard might look like instead. Weaver, who also teaches graduate students at the University of Toronto and created the AID Framework for AI disclosure, walks through the practical and philosophical challenges of building trust infrastructure for an ecosystem that doesn't have bright lines yet. The conversation covers disciplinary divides in how AI use is understood, the global effort to establish a disclosure standard, and why the authorship question remains genuinely unresolved.Key Takeaways:Citation can't bridge the gap between AI-generated ideas and their sources. Traditional citation connects ideas to a discrete, traceable origin. AI severs that connection by synthesizing across sources in ways that can't be pinpointed. Weaver notes this is structurally similar to what Western scholarship has long done to traditional and lived knowledge -- and now researchers are experiencing that same disconnection applied to their own work.A global AI disclosure standard is actively being built. Weaver is co-leading a large-scale effort with the European Network of Research Integrity Offices, the International Science Council, and the Committee on Publication Ethics to develop a consistent disclosure framework through the World Conferences on Research Integrity. The goal is to stop researchers from having to tailor disclosures to each journal's idiosyncratic requirements.AI use in research often falls outside methodology entirely. A researcher translating articles from an unfamiliar language using AI is a real and beneficial use case, but it doesn't fit neatly into a methods section. These peripheral...","thumbnail_url":"https://img.transistorcdn.com/mIvclI2fK-fQrurJTjPiYoTWWGoNWSdbv1_-Xa6ULdc/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8yOTNk/OTcyZTcxOWE5MGIw/ZTY0MjU4ZGNlN2U5/NjM3My5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}