There’s a reason I stayed up all night on my birthday writing that article (nitter.cf/furongh/status/2101913…): I’ll be serving as an ICML 2027 Program Chair, alongside Weijie Su, Andrew Wilson, Julien Mairal, and Courtney Paquette. As someone who has worked on AI detection and will help run the conference, I feel a real responsibility to think more deeply about “AI slop” and what we should actually do about it. With reports of 60K+ abstract submissions to ICLR 2027 (linkedin.com/posts/gdemelo_o…), how do we scale peer review without lowering the bar or burning people out? Recruiting enough reviewers is one challenge. Matching papers to the right expertise is another. Here’s where I stand. Using AI to improve your writing should not be punished. This matters especially for non-native English speakers. If AI contributed meaningfully to your ideas, experimental design, or analysis, be transparent about it. Either way, you are responsible for understanding, checking, and standing behind the work. But **we should not ask reviewers to spend hours on a paper its own authors haven’t read, checked, or understood**. That standard should apply whether AI was involved or not. Submitting a paper is also a request for someone else’s time. And “just run it through an AI detector” is not the answer. Research has documented false positives, including for non-native English writing, and ways to evade detection. A detector score is not a verdict on scientific quality. (arxiv.org/abs/2304.02819) Nor should confidential submissions be uploaded to external services without authorization and appropriate privacy safeguards. (icml.cc/Conferences/2026/Rev…) I think desk rejection deserves serious consideration, but it needs to be based on demonstrable problems, not “this sounds AI-written.” What can we reliably identify before full review? Who makes those calls, and how do we catch mistakes? We need to protect reviewer time without screening out unconventional work just because it is unfamiliar. **I’d love concrete suggestions on reviewer matching, early triage, and author accountability.** What have other venues tried? What worked and what backfired? How do we discourage careless submissions without making it harder for newcomers and less-resourced researchers to participate? These are my personal starting views, not an announcement of ICML policy. We’ll do our best to serve the community, and your input would genuinely help. #ICML2027