Researchers question trusting OpenAI with unpublished math
Original: More questions about whether researchers can trust OpenAI with unpublished math
Why This Matters
Academic trust in AI labs is critical as research communities increasingly interact with commercial AI tools.
Mathematician Andreas Thom raised concerns on Mathstodon about whether academic researchers can safely share unpublished mathematical work with OpenAI, reigniting debate over data confidentiality and research integrity in AI collaborations.
On Mathstodon, mathematician Andreas Thom posted a thread questioning whether researchers can trust OpenAI with unpublished mathematical results. The post, directed at another user, appears to be part of an ongoing conversation about the risks of sharing sensitive, pre-publication research with AI companies. The concern centers on whether OpenAI's data handling practices provide sufficient confidentiality guarantees for academic work that has not yet been formally published or peer-reviewed. The discussion reflects broader anxieties in the research community about submitting original work to AI systems — particularly when such systems may use submitted data for training or other purposes. While the full thread content is limited due to platform rendering requirements, the exchange signals growing unease among mathematicians and scientists about the terms under which they engage with large AI labs. No specific incident or breach was cited in the available content, but the framing suggests accumulated skepticism rather than a single triggering event.