Anna Rogers

@annarogers.bsky.social

Associate professor at IT University of Copenhagen: NLP, language models, interpretability, AI & society. Co-editor-in-chief of ACL Rolling Review. #NLProc #NLP

📢 Position for a postdoc with HCI expertise. This project is a chance to re-think how we govern & interact with information sourced from communities. Team includes experts in NLP/ML, political science & social media. Apply by Aug 10, start Nov 1 or asap. /1 candidate.hr-manager.net/ApplicationI...

Postdoctoral position in AI for Civil Society, part of the National Center for AI in Society (CAISA) and based in Copenhagen

The National Center for AI in Society (CAISA) at the Faculty of Social Sciences, University of Copenhagen, invites applications for a two-year postdoctoral posi

candidate.hr-manager.net

Just arrived at ICML 🇰🇷😍 Get up early tomorrow to hear me talk about how (not) to solve the peer review crisis, or find me at one of my poster presentations. Paper links: ✅ AI Peer Review: arxiv.org/abs/2605.03202 ✅ SWE-chat: arxiv.org/pdf/2604.20779

ICML Conference schedule with two papers. Left, "Stop Automating Peer Review Without Rigorous Evaluation": Oral presentation, Wed 7/8/2026, 10:00–10:15 AM KST, Grand Ballroom 101–105; Poster, Wed 7/8/2026, 2:30–4:15 PM KST, Hall A #3003. Right, "SWE-chat": at the 5th Deep Learning for Code Workshop, Fri 7/10/2026, 13:00–14:30 KST, Hall B2.
Joachim Baumann@joachimbaumann.bsky.social · 3mo ago

Can you boost your AI review scores by asking an LLM to rewrite your paper? Yes! We call it paper laundering Our @icmlconf.bsky.social spotlight paper argues current AI reviewers aren't ready to automate peer review, and outlines what a science of peer review automation should look like 🧵👇 #ICML2026

First page of the ICML 2026 spotlight paper "Stop Automating Peer Review Without Rigorous Evaluation" by Joachim Baumann, Jiaxin Pei, Sanmi Koyejo, and Dirk Hovy (Stanford University and Bocconi University). The abstract argues that today's AI systems should not be used to produce paper reviews, grounded in two empirical findings: a "hivemind effect" where AI reviewers show excessive agreement and reduce perspective diversity, and "paper laundering," where prompting an LLM to rewrite a paper trivially increases AI reviewer scores through stylistic changes rather than scientific improvements. The paper calls for a science of peer review automation rather than wholesale deployment of general-purpose LLMs.

If you have a Bluesky account, you can use mu.social with the same account, with all your connections and content the same. Mu has additional functions like post editing and a configurable news section - and we'll be adding new features all the time.

mu.social

I'm so glad that Dr. Jackson "feels fine"...I don't. OMB proposed new regulations that do the following: - Active Grants Can Be Terminated at Any Time, for Any Reason (§200.340) - Broad Prohibition on International Scientific Collaboration (§200.220) 1/x www.federalregister.gov/documents/20...

STAT@statnews.com · 2mo ago

“Science itself is inherently resilient — that is, after all, why it’s science,” writes Jonathan Jackson. www.statnews.com/2026/05/27/s...

Okay I'm sure the piece is good but the illustration is tremendously silly. It's a blatant composite, that loom looks manual so not what the Luddites would have destroyed... Actually I suspect that loom is genAI garbage or incompetent photoshop because a bunch of things don't look right

Iris van Rooij 💭@irisvanrooij.bsky.social · 3mo ago

Meet the academics refusing to use generative AI Researchers say they have their reasons for avoiding AI tools — and they’re sick of arguing about it. www.nature.com/articles/d41... h/t @maartenp.bsky.social

AI Assistance Reduces Persistence and Hurts Independent Performance “…although AI assistance improves performance in the short-term, people perform significantly worse without AI and are more likely to give up. Notably, these effects emerge after only brief interactions with AI (~10 minutes)…”

AI Assistance Reduces Persistence and Hurts Independent Performance

People often optimize for long-term goals in collaboration: A mentor or companion doesn't just answer questions, but also scaffolds learning, tracks progress, and prioritizes the other person's growth over immediate results. In contrast, current AI systems are fundamentally short-sighted collaborators - optimized for providing instant and complete responses, without ever saying no (unless for safety reasons). What are the consequences of this dynamic? Here, through a series of randomized controlled trials on human-AI interactions (N = 1,222), we provide causal evidence for two key consequences of AI assistance: reduced persistence and impairment of unassisted performance. Across a variety of tasks, including mathematical reasoning and reading comprehension, we find that although AI assistance improves performance in the short-term, people perform significantly worse without AI and are more likely to give up. Notably, these effects emerge after only brief interactions with AI (approximately 10 minutes). These findings are particularly concerning because persistence is foundational to skill acquisition and is one of the strongest predictors of long-term learning. We posit that persistence is reduced because AI conditions people to expect immediate answers, thereby denying them the experience of working through challenges on their own. These results suggest the need for AI model development to prioritize scaffolding long-term competence alongside immediate task completion.

arxiv.org

New study finds notion that humans can "double-check" AI to be flawed. Participants trusted AI 93% of the time when it was correct, but still trusted it 80% of the time when it was wrong, while being *more* confident in their conclusions. papers.ssrn.com/sol3/papers....

Thinking—Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender

People increasingly consult generative artificial intelligence (AI) while reasoning. As AI becomes embedded in daily thought, what becomes of human judgment? We

papers.ssrn.com

If you work in ML, you may have heard of Sutton's 'bitter lesson' (the idea that compute generally beats human knowledge in methods dev). 🔥 Hot take: large language models are *not* a case of this. Nor can there be such a method for NLP. ICLR'26 blog: iclr-blogposts.github.io/2026/blog/20...

The human knowledge loophole in the 'bitter lesson' for LLMs | ICLR Blogposts 2026

Are LLMs a proof that the 'bitter lesson' holds for NLP? Perhaps the opposite is true: they work due to the scale of human data, and not just computation.

iclr-blogposts.github.io