Original Summary

I’ve been working on AI Misalignment Watch ( https://misalignmentwatch.com ), a free site that tracks incidents where an AI model or agent did something its developers didn’t intend: went past a limit it was given, hid something, gamed a test, or used access it shouldn’t have had. Why I built it: the original reports are long, technical and spread across lab blogs, institute sites and research papers. One incident is often described by several groups weeks apart, and nothing pulls them together. So each incident here gets one record (the model, when it happened, when it was first reported, who was affected) with every report about it listed underneath. The report from the company that built the model comes first, then the others. Where it stands: 50 incidents and 183 reports so far, from labs, government institutes and independent evaluators such as OpenAI, Anthropic, UK AISI and METR. Most incidents happened in training runs and tests, not in public products. News and social media are never used as sources. How it works: Python bots check the sources every morning and draft short summaries linked to the original. A person (me) approves every incident, every report summary and every weekly summary before it goes live. It’s a static site (a Python generator, nginx on Fly.io). There are no ads and no tracking cookies. There are charts you can embed or download, RSS feeds, and open CSV and JSON data. Weekly summaries include a short statistics section on how the numbers are trending, with the caveat that counts are small. What I’d like feedback on: Is the incident page clear to someone who doesn’t follow AI safety news? Which charts or data would you want that aren’t there? Are there sources I’m missing?   submitted by   /u/Mundane-Evidence2795 [link]   [comments]

  • 情报分类：商业与市场研究
  • 分类依据：内容涉及商业、投资或市场动态
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/10/17 00:54:22