emanuelr

How many AI safety papers are at the big ML conferences, what do they study, and who writes them? A comprehensive analysis. > Website: https://ai-safety-tracker-website.vercel.app/ > Data, code and plots: https://github.com/SomaxSoma/AI-Safety-Research-Tracker TL;DR: We classified every paper accepted at ICLR, ICML and NeurIPS from 2019 through 2026, using an LLM that reads each title and abstract. 2,328 of them (4.2%) are AI safety papers. Safety's share of accepted papers grew from 0.3% in 2019 to 8.3% in 2026, roughly a 25-fold increase. This post is a reference for the main results. The interactive website lets you browse every paper, its subdomain, and the classifier's reasoning. What this is We wanted to have an overview of what is going on in AI safety research while being as broad as possible. This seems necessary to be able to prioritize research correctly and develop more precise theories of change. One way to achieve this is by analyzing statistics of all the published main-conference papers on the topic of AI safety, but we couldn't find a dataset that actually measured them. So we built one: - Every accepted paper at ICLR (2019–2026), ICML (2019–2026) and NeurIPS (2019–2025) (55,794 in total) is read by an LLM (DeepSeek V4 Flash), which classifies it from its title and abstract into one of four classes: AI safety (frontier & misalignment), truthfulness, reliability & XAI, ethics & fairness, or general capabilities. - Each safety paper is then assigned one of 17 safety subdomains (interpretability, alignment training, scalable oversight, dangerous-capability evals, …) and scored on a 7-point rubric for how centrally it addresses safety. - A separate full-text pass over the safety papers detects which safety organizations, programs and funders are behind them, distinguishing affiliations and acknowledgments. All the code required to reproduce these results (classification prompts, CSVs, etc.) is publicly available. The website has a searchable exp