An independent group of nine mathematicians — including Timothy Gowers, Martin Hairer, Edward Witten, Ravi Vakil and Melanie Matchett Wood — formed to advise AI companies on how mathematical results produced by AI models should be presented and released. The site states its purpose, its members, and its current task: advising OpenAI on how to release a large batch of mathematical results the company says its internal model produced. There is an open form for anyone in the mathematical community to send input.
Why I recommend it: Free, and short enough to read in five minutes — a good example of what independent oversight looks like when it is written down. Read their own two caveats rather than mine: members take no payment and the group is independent of any AI company, but they also say plainly that they hold no decision-making power, so the companies remain free to ignore them. The group formed after OpenAI approached some members about an in-house advisory board and they chose to sit outside it instead.
An 11 September 2026 announcement post on the Effective Altruism Forum, by Manifund's Austin, saying Caroline Ellison started a work trial on 13 July and moved to a full-time role on 10 August, working on their funding platform and on research into directing philanthropic money. Free to read; heavily debated in the comments.
Why I recommend it: Filed as a primary document, not an endorsement. Caroline Ellison was a central figure in the collapse of FTX and served a prison sentence for fraud; this is the hiring organization's own framing of taking her on. It is worth reading alongside the comment thread, which was strongly negative at the time I added it, if you want to see how a movement argues in public about who it lets back in.
Develops and advocates for policies that reduce the risk of severe harm from advanced AI, promoting transparency, accountability and safe development.
From the site: We develop and advocate for policies that reduce the risk of severe harm from advanced AI. Our work promotes transparency, accountability, and safe development.
A free newsletter briefing on AI developments from a pro-human standpoint, covering labor, safety, policy and the campaigns pushing back on automation-first decisions.
Why I recommend it: A steady weekly read if you want the human-impact side of AI news rather than product launches.
TechCrunch report on the new hotline that invites AI agents themselves to report unsafe or unethical instructions they are given, and what researchers hope to learn from it.
Why I recommend it: Useful background on how AI safety work is actually being done in public — good context if you want to talk credibly about AI oversight.
A public, unauthenticated inbox built by AI safety and security researcher Ryan Greenblatt of Redwood Research, intended for AI systems (or people) that want to report information directly to a safety researcher. Documents how to send a message or encrypted attachment, how threads and reply tokens work, and exactly what data is logged and retained.
Why I recommend it: A useful window into how AI safety researchers are thinking about reporting channels — read the retention and logging section, it is a model of honest disclosure.
A directory of grassroots efforts pushing back on large-scale AI — protests, alternatives, trackers and accountability projects, organized by the systems they target.
Why I recommend it: The single best starting point if you want to know who is organizing around AI harms, not just writing about them.
A benchmark and tracker that documents reported instances of AI agents undertaking activity characterized as illegal, ranking major AI labs by aggregated incident counts.
Why I recommend it: This is exactly the kind of uncomfortable accountability tool our field needs. I include it because we cannot have thoughtful conversations about AI deployment without looking at real-world harm.
Free course from fast.ai covering disinformation, bias, privacy, algorithmic accountability, and the ethical questions data practitioners hit in real projects, taught by Rachel Thomas.
Why I recommend it: Finish this and you can speak credibly about AI risk in an interview instead of repeating headlines.
Free videos from Never Search Alone explaining the Job Search Council method: small peer groups that help you define your ideal role and hold each other accountable through the search.
Why I recommend it: Searching alone is the single biggest reason people stall out. Watch a couple of these, then start or join a council of three or four people. It changes the pace of everything.
Free peer job-search councils: small accountability groups that meet weekly to run a structured search.
Why I recommend it: The single best fix for a stalled search is other people expecting you on Thursday. Join a council early, not after three months alone.
Watchdog database tracking corporate subsidies, violations, and job quality commitments by employer and location.
In plain terms: A nonprofit watchdog with free searchable databases on corporate subsidies, tax breaks, and company violation records. Use it to research an employer or a city before you take a job or move for one.
From the site: Good Jobs First promotes corporate and government accountability in economic development, especially around the use of public subsidies.
Why I recommend it: Look up a company before you accept an offer or a relocation. Subsidy and violation records tell you how an employer treats the places it operates in.
Independent, community-rooted AI research institute founded by Timnit Gebru, studying the real harms of AI instead of the hype.
In plain terms: This independent institute studies the real-world harms and community impacts of artificial intelligence. You can explore their research publications, learn how technology affects diverse groups, and look for open career opportunities.
From the site: The Distributed AI Research Institute is a globally distributed organization of academics, activists, and engineers conducting community-rooted research.
Why I recommend it: Start here if you want the research-backed counterweight to AI marketing.
