The conditions in this letter are critical if we're going to take the idea of embedded evaluations seriously. We strongly support efforts to ensure meaningful and genuinely independent oversight of frontier AI companies. We are proud to be chairing the @aievalforum and greatly appreciate our many collaborators on this effort.

“When companies promise that they’re going to do a better job on safety, we shouldn’t have to take their word for it.” - @random_walker on @CBSNews discussing today’s AEF letter and our call for credible, independent AI evaluations.

Trust is essential and it’s earned through transparency, independence, and the freedom to speak truth to power. AEF is asking AI developers to provide these basic guarantees to ensure AI evaluations can be done credibly and to the highest scientific standards. This shouldn't be controversial.

I personally think it’s important that independent groups (from all backgrounds) get access to inform the public about what’s going on without it being filtered through layers of PR or with things being swept under the rug.

i'm heartened to see ai companies agree *that* we need embedded evaluations; now the question becomes *how.* answering this question requires many voices. today 100+ respond, calling for five minimum requirements to make embedded evaluations credible. we have a lot of work to do. to meet this demand, @aievalforum -- where i act as senior advisor -- is embarking on an ambitious path ahead to: (1) develop standards for embedded evaluation; (2) create independence-preserving funding mechanisms; and (3) establish a talent pipeline for independent evaluation. we are actively expanding our membership. we also welcome conversations on support and funding. please reach out if you would like to collaborate with us on any of these initiatives.

I do not like signing open letters, because they are typically pretty performative and pointless. This one is not. It is substantive and timely: if we're going to rely on embedded evaluators for safety assurance, then these at least are the minimum conditions for doing so. At a time when states and national governments are considering legislating on this, heeding this call would, I think, be a very good idea.

OpenAI and Anthropic have committed to bringing in embedded external assessors. What should that involve in practice? The AI Evaluator Forum brought together a bunch of experts (and myself) for a short joint statement on it.

I don’t think that embedding evaluators at AI companies will be sufficient to give the public the visibility or assurance it deserves right now. But if we want to scale embedded evaluations and rely on them down the line, I definitely want them to be transparent.

over the past week, I’ve gotten a lot of questions about what independent AI evaluations should actually look like in practice. what about conflicts of interest between labs and evaluators? will evaluators actually get meaningful access? what if companies just block the publication of unfavorable findings? these are good and important questions. and we shouldn’t assume voluntary arrangements alone will produce the conditions that credible independent evaluation requires. so i’m very excited to see 100+ researchers, evaluators, and experts — including us at @AVERIorg — laying out minimum conditions for embedded evaluation, including: meaningful independence, strong access, transparency / editorial control, protections against retaliation, and a diversity of perspectives and competencies. and we shouldn’t try to incorporate those principles in ad hoc, one-off agreements, policy is critical to standardizing these safeguards across the industry. glad to see @aievalforum leading on putting these principles on paper.

Happy to sign this. Truly independent evaluation and oversight of frontier AI is a crucial issue. I think one of the biggest bottlenecks is finding people with the technical capabilities and that high bar of independence. AI Eval Forum is a good group to build initial momentum!

I'm one of 100+ signatories to a public letter calling for five minimum requirements to make embedded evaluations credible, by establishing evaluator independence, protection from retaliation, and real access. ———— We, the undersigned, are encouraged to see frontier AI companies call for embedding third-party organizations to evaluate rapidly escalating AI capabilities and risks. We believe that all frontier AI companies should embed evaluators to independently assess AI risks, including evaluating the systems themselves and any significant incidents of real-world harm, as well as the companies’ training, deployment, oversight, operational, and safeguard practices. To be credible, embedded third-party evaluations must have scientific objectivity, transparency, independence, and robust protections against interference from the evaluated companies, including at least: 1. Frontier AI companies should rely on evaluators that are meaningfully independent, that maintain full editorial control, and that disclose and mitigate potential conflicts of interest. This includes at a minimum that embedded evaluation organizations should not be owned or governed by frontier AI companies, should not have other significant commercial business with them, and should not accept any form of payment or other reward contingent on the evaluator’s findings. 2. Frontier AI companies should incorporate differing viewpoints and areas of expertise, including by embedding multiple evaluation organizations across a range of priority risk areas, each with deep relevant technical expertise, as well as by allowing and encouraging evaluators to share how conclusions differ among evaluators and between evaluators and company employees. 3. Embedded evaluators should be transparent, including transparency about their methods and findings, the nature of their access, and the broader terms of the evaluation. Frontier AI companies should actively facilitate this transparency, including limiting the scope of non-disclosure agreements. They should also allow evaluators prompt and unfiltered communication with the companies’ boards and other privileged oversight bodies, as well as public release of findings and evidence, subject only to a time-limited redaction process restricted to protecting critical interests in intellectual property, customers’ sensitive information, individual privacy, security, and public safety. 4. Embedded evaluators should be shielded from retaliation from the companies they embed with for choosing reasonable evaluation methods, discovering information, or drawing conclusions that are unflattering to those companies. This includes reasonable protections against retaliatory litigation, as well as funding mechanisms that give them confidence they will remain funded even in these cases. 5. Frontier AI companies should grant embedded evaluators access equivalent to that of their own highly privileged employees for the purposes of their evaluations, and with exceptions to protect sensitive data belonging to the company’s customers and other third parties. This includes access to the same relevant systems, data, tools, and physical spaces as those available to senior internal company employees responsible for carrying out comparable risk assessments, as well as candid and direct one-on-one communication with relevant staff. This list is not comprehensive, and conditions like these to ensure credible evaluations should be increasingly standardized, codified, and enforced. One example is the set of terms defined in the AEF-1 standard, which has already seen early adoption, but far more work will be necessary to ensure that embedded evaluators are effective and meaningful. Embedded evaluations cannot address all oversight needs and should be treated as a complement to, rather than a replacement for, broader efforts by frontier AI companies to expand external oversight, including greater public transparency and additional, broader forms of access for independent researchers. ———— Full letter with links and signatures: I'm grateful to the AI Evaluator Forum (@aievalforum) for organizing this.

Today, more than 100 leading AI experts endorsed a set of minimum requirements to take seriously AI companies' recent call to embed external evaluators. These evaluators need to be genuinely independent, transparent, and represent a range of expertise areas. They also need to be guaranteed employee-level access and to be protected from retaliation for findings that make companies look bad. We welcome model developers’ recent calls for independent oversight, but it’s what they do next that matters. The labs must be accountable for ensuring these requirements are met, so that the public can have faith in the process and the outcomes. Over the past week, the AI community has debated the appropriate role of external evaluation, including who should do it and on what terms. We may not agree on everything, but there is a lot of common ground. To make embedded evaluations credible, more than 100 experts with varying backgrounds and ideas about AI risk agree in today’s letter that frontier AI developers should: 1. Guarantee embedded evaluators full editorial independence and mitigate conflicts of interest 2. Rely on multiple evaluators with differing viewpoints and areas of expertise 3. Publicly document the terms under which evaluators operate, as well as facilitating permissive publication of methods and findings 4. Shield evaluators from retaliation 5. Grant access equivalent to that of highly privileged employees There is a thriving and growing ecosystem of independent AI evaluators who are advancing this science every day – but we need aligned standards, guaranteed protections, and independent funding. That’s why we created the AI Evaluator Forum. Today we are entering our next phase. We’re launching an open call for new members, collaborators, and independent funding sources to help evaluators meet this moment and demand accountability from developers. Join us in building the evaluator ecosystem. See the public letter here: Learn more at

The AI Evaluator Forum (AEF) welcomes recent statements regarding the importance of embedded independent experts to verify AI safety and security claims. This is a first step towards trustworthy oversight of frontier AI. No single evaluator can do this work alone. A vibrant ecosystem of independent evaluators from distinct backgrounds can offer a range of expertise and methodology, help to ensure rigor, and avoid a single point of failure. The Forum exists to strengthen this ecosystem. Realizing the benefits of third-party evaluations also requires independence, access, and transparency. AEF brings evaluators together to address these questions. Our first standard, AEF-1: Minimum Operating Conditions for Independent Third-Party AI Evaluations, envisions a minimum floor for access, managing conflicts of interest, funding relationships, recusal requirements, and transparency of evaluation terms. Read more here: The long-term success of third-party evaluation relies on standards and frameworks. AEF is committed to developing these with its members and collaborators. We welcome engagement from organizations committed to rigorous, independent work. Learn more about AEF, express interest in joining, and raise questions for us here:

We need rigorous, transparent evaluation if we want the world to understand advanced AI capabilities and risks. We're excited to join with other independent evaluators through the AI Evaluator Forum to raise the bar on measurement best practices.