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        testing as a key ingredient of AI policy\n\nMar 25, 2024●18 min read\n\n\n\nWe
        believe that the AI sector needs effective third-party testing for frontier
        AI systems. Developing a testing regime and associated policy interventions
        based on the insights of industry, government, and academia is the best way
        to avoid societal harm—whether deliberate or accidental—from AI systems.\n\n\n\nOur
        deployment of large-scale, generative AI systems like Claude has shown us
        that work is needed to set up the policy environment to respond to the capabilities
        of today’s most powerful AI models, as well as those likely to be built in
        the future. In this post, we discuss what third-party testing looks like,
        why it’s needed, and describe some of the research we’ve done to arrive at
        this policy position. We also discuss how ideas around testing relate to other
        topics on AI policy, such as openly accessible models and issues of regulatory
        capture.\n\n\n\nPolicy overview\n\n\n\nToday’s frontier AI systems demand
        a third-party oversight and testing regime to validate their safety. In particular,
        we need this oversight for understanding and analyzing model behavior relating
        to issues like election integrity, harmful discrimination, and the potential
        for national security misuse. We also expect more powerful systems in the
        future will demand deeper oversight - as discussed in our ‘Core views on AI
        safety’ post, we think there’s a chance that today’s approaches to AI development
        could yield systems of immense capability, and we expect that increasingly
        powerful systems will need more expansive testing procedures. A robust, third-party
        testing regime seems like a good way to complement sector-specific regulation
        as well as develop the muscle for policy approaches that are more general
        as well.\n\n\n\nDeveloping a third-party testing regime for the AI systems
        of today seems to give us one of the best tools to manage the challenges of
        AI today, while also providing infrastructure we can use for the systems of
        the future. We expect that ultimately some form of third-party testing will
        be a legal requirement for widely deploying AI models, but designing this
        regime and figuring out exactly what standards AI systems should be assessed
        against is something we’ll need to iterate on in the coming years - it’s not
        obvious what would be appropriate or effective today, and the way to learn
        that is to prototype such a regime and generate evidence about it.\n\n\n\nAn
        effective third-party testing regime will:\n\n\n\nGive people and institutions
        more trust in AI systems\n\nBe precisely scoped, such that passing its tests
        is not so great a burden that small companies are disadvantaged by them\n\nBe
        applied only to a narrow set of the most computationally-intensive, large-scale
        systems; if implemented correctly, the vast majority of AI systems would not
        be within the scope of such a testing regime\n\nProvide a means for countries
        and groups of countries to coordinate with one another via developing shared
        standards and experimenting with Mutual Recognition agreements\n\n\n\nSuch
        a regime will have the following key ingredients [1]:\n\n\n\nEffective and
        broadly-trusted tests for measuring the behavior and potential misuses of
        a given AI system\n\nTrusted and legitimate third-parties who can administer
        these tests and audit company testing procedures\n\nWhy we need an effective
        testing regime\n\n\n\nThis regime is necessary because frontier AI systems—specifically,
        large-scale generative models that consume substantial computational resources—don’t
        neatly fit into the use-case and sector-specific frameworks of today. These
        systems are designed to be ''everything machines'' - Gemini, ChatGPT, and
        Claude can all be adapted to a vast number of downstream use-cases, and the
        behavior of the downstream systems always inherits some of the capabilities
        and weaknesses of the frontier system it relies on.\n\n\n\nThese systems are
        extremely capable and useful, but they also present risks for serious misuse
        or AI-caused accidents. We want to help come up with a system that greatly
        reduces the chance of major misuses or accidents caused by AI technology,
        while still allowing for the wide deployment of its beneficial aspects. In
        addition to obviously wanting to prevent major accidents or misuse for its
        own sake, major incidents are likely to lead to extreme, knee-jerk regulatory
        actions, leading to a ''worst of both worlds'' where regulation is both stifling
        and ineffective. We believe it is better for multiple reasons to proactively
        design effective and carefully thought through regulation.\n\n\n\nSystems
        also have the potential to display emergent, autonomous behaviors which could
        lead to serious accidents - for instance, systems might insert vulnerabilities
        into code that they are asked to produce or, when asked to carry out a complex
        task with many steps, carry some actions which contradict human intentions.
        Though these kinds of behaviors are inherently hard to measure, it’s worth
        developing tools to measure for them today as insurance against these manifesting
        in widely deployed systems.\n\n\n\nAt Anthropic, we’ve implemented self-governance
        systems that we believe should meaningfully reduce the risk of misuse or accidents
        from the technologies we’ve developed. Our main approach is our Responsible
        Scaling Policy (RSP), which commits us to testing our frontier systems, like
        Claude, for misuses and accident risks, and to deploy only models that pass
        our safety tests. Multiple other AI developers have subsequently adopted or
        are adopting frameworks that bear a significant resemblance to Anthropic''s
        RSP.\n\n\n\nHowever, although Anthropic is investing in our RSP (and other
        organizations are doing the same), we believe that this type of testing is
        insufficient as it relies on self-governance decisions made by single, private
        sector actors. Ultimately, testing will need to be done in a way which is
        broadly trusted, and it will need to be applied to everyone developing frontier
        systems. This type of industry-wide testing approach isn’t unusual - most
        important sectors of the economy are regulated via product safety standards
        and testing regimes, including food, medicine, automobiles, and aerospace.\n\n\n\nWhat
        would a robust testing regime look like?\n\n\n\nA robust third-party testing
        regime can help identify and prevent the potential risks of AI systems. It
        will require:\n\n\n\nA shared understanding across industry, government, and
        academia of what an AI safety testing framework looks like - what it should
        and shouldn’t include\n\nAn initial period where companies complete practice
        runs of implementing such testing, sometimes with third-party oversight, to
        make sure the tests work, are feasible to run, and can be validated by a third
        party\n\nA two-stage testing regime: there should be a very fast, automated
        testing stage that companies apply to their systems. This stage should cover
        a wide area and be biased towards avoiding false negatives. If this stage
        spots potential problems, there should be a more thorough secondary test,
        likely using expert human-led elicitation\n\nIncreased resources to the parts
        of government that will oversee and validate tests - building and analyzing
        tests is detailed, expensive, technical work, so governments will need to
        find a way to fund the entities that do this\n\nA carefully scoped set of
        mandated tests - we’ll need specific, legally mandated tests where it becomes
        clear there are poor incentives for industry self-governance, and the benefits
        of public safety from government oversight outweigh the regulatory burdens.
        We should ensure this is a well scoped, small set of tests, or else we’ll
        create regulatory burdens and increase the possibility of regulatory capture\n\nAn
        effective balance of the assurance of safety with ease of administration of
        these tests\n\n\n\nWhen it comes to tests, we can already identify one area
        today where testing by third-parties seems helpful and draws on the natural
        strengths of governments: national security risks. We should identify a set
        of AI capabilities that, if misused, could compromise national security, then
        test our systems for these capabilities. Such capabilities might include the
        ability to meaningfully speed up the creation of bioweapons or to carry out
        complex cyberattacks. (If systems are capable of this, then that would lead
        to us changing how we deployed the model - e.g, remove certain capabilities
        from broadly deployed models and/or gate certain model capabilities behind
        ‘know your customer’ regimes, and ensuring relevant government agencies were
        aware we had systems with these capabilities.) We expect there are several
        areas where society will ultimately demand there be legitimate, third-party
        testing approaches, and national security is just one of them.\n\n\n\nWhen
        it comes to the third party doing the testing, there will be a multitude of
        them and the tests will be carried out for different reasons, which we outline
        here:\n\n\n\nPrivate companies: Companies may subcontract other companies
        to build tests and evaluations for their systems, as we have done with firms
        like Gryphon Scientific. We can also imagine companies doing tests for other
        companies where the tests are mandated by law but not carried out by government
        agencies, similar to how accounting firms audit the books of private companies.\n\nUniversities:
        Today, many researchers at many academic institutions have free or subsidized
        access to models developed by AI labs; in the future, we could imagine some
        of these research institutions administering their own testing initiatives,
        some of which may be supervised or elevated via government bodies.\n\nGovernments:
        Some tests (we suspect, a relatively small number) may be mandated by law
        and carried out by government actors - for instance, for testing for national
        security misuses of AI systems. Here, government agencies may carry out the
        tests directly.\n\n\n\nUltimately, we expect that third-party testing will
        be accomplished by a diverse ecosystem of different organizations, similar
        to how product safety is achieved in other parts of the economy today. Because
        broadly commercialized, general purpose AI is a relatively new technology,
        we don’t think the structure of this ecosystem is clear today and it will
        become clearer through all the actors above running different testing experiments.
        We need to start working on this testing regime today, because it will take
        a long time to build.\n\n\n\nWe believe that we - and other participants in
        AI development - will need to run multiple testing experiments to get this
        right. The stakes are high: if we land on an approach that doesn’t accurately
        measure safety but is easy to administer, we risk not doing anything substantive
        or helpful. If we land on an approach that accurately measures safety but
        is hard to administer, we risk creating a testing ecosystem that favors companies
        with greater resources and thus reduces the ability for smaller actors to
        participate.\n\n\n\nHow Anthropic will support fair, effective testing regimes\n\n\n\nIn
        the future, Anthropic will carry out the following activities to support governments
        in the development of effective third-party testing regimes:\n\n\n\nPrototyping
        a testing regime via implementing our RSP and sharing what we learn\n\nTesting
        third-party assessment of our systems via contractors and government partners\n\nDeepening
        our frontier red teaming work to give us and the broader sector a clearer
        sense of the risks of AI systems and their mitigations\n\nAdvocating for governments
        to fund the agencies and organizations that could help to develop an effective
        third-party testing regime (e.g, in the United States, NIST, the US AI Safety
        Institute, the National AI Research Resource, the usage of DoE supercomputers
        for AI testing, and so on)\n\nEncouraging governments to build their own ‘National
        Research Clouds’ (like the National AI Research Resource in the US) so that
        they can a) develop independent capacity in academia and government to build,
        study, and test frontier AI systems, and b) work on the science of evaluating
        AI systems, including those developed by private companies like Anthropic\n\n\n\nDeveloping
        a testing regime and associated policy interventions based on the insights
        of industry, government, and academia is the best way to avoid societal harm—whether
        deliberate or accidental—from AI systems.\n\n\n\nHow testing connects to our
        broader policy priorities\n\n\n\nOur overarching policy goal is to have appropriate
        oversight of the AI sector. We believe this will mostly be achieved via there
        being an effective ecosystem for third-party testing and evaluation of AI
        systems. Here are some AI policy ideas you can expect to see us advocating
        for in support of that:\n\n\n\nGreater funding for AI testing and evaluation
        in government\n\n\n\nEffective testing and evaluation procedures are a necessary
        prerequisite of any effective form of AI policy. We think that governments
        should stand up and support institutions that develop AI evaluations, as well
        as bringing together industry, academia, and other stakeholders to agree on
        standards for the safety of AI systems. In the US, we specifically advocate
        for greater funding for NIST.\n\n\n\nSupport greater evaluation of AI systems
        through public sector infrastructure for doing AI research\n\n\n\nWe urgently
        need to increase the number and breadth of people working to test and evaluate
        AI systems, for both current and future risks. It’s therefore crucial that
        governments create experimental infrastructure to help academic researchers
        test out and evaluate frontier AI systems, and develop their own frontier
        systems for beneficial purposes. For more information, see our support for
        a US national research cloud via the CREATE AI Act, and our written Senate
        testimony.\n\n\n\nDeveloping tests for specific, national security-relevant
        capabilities\n\n\n\nWe should know if AI systems can be used in ways that
        strengthen or (if fielded by another entity) weaken national security. Whereas
        the private sector and academia can develop the vast majority of tests, some
        testing and evaluation questions relate to national security capabilities
        which are classified, so only certain governments are able to effectively
        evaluate them. Therefore, we want to support US Government efforts to develop
        ways of testing AI systems for national security-relevant capabilities. We
        will also continue our own work to better understand the capabilities of our
        own systems.\n\n\n\nScenario planning and test development for increasingly
        advanced systems\n\n\n\nOur Responsible Scaling Policy is designed to frontload
        work about evaluating and testing future, hypothetical capabilities of AI
        systems. This is to ensure we have the relevant tests to better assess and
        minimize accident and misuse risks from increasingly powerful AI systems.
        But we don’t claim that our RSP delineates all the tests that need to be run
        on increasingly powerful models. As AI advances driven by growing computational
        power increase, a broader set of actors should work to anticipate the future
        capabilities of AI systems, and develop tests for them.\n\nAspects of AI policy
        we believe are important to discuss\n\n\n\nWhile developing our policy approach,
        we’ve also found ourselves returning again and again to a few specific issues
        such as openly accessible models and regulatory capture. We’ve outlined our
        current policy thinking below but recognize these are complicated issues where
        people often disagree.\n\n\n\nOpenly-disseminated and/or open-source models:
        Science moves forward largely due to a culture of openness and transparency
        around research. This is especially true in AI, where much of the currently-unfolding
        revolution is built on the open publication of research and models like the
        Transformer, BERT, Vision Transformers, and so on. There is also a long history
        of open source and openly accessible systems increasing the robustness of
        the security environment by helping a greater number of people experiment
        with technologies and identify their potential weaknesses.\n\n\n\nWe believe
        that the vast majority of AI systems today (perhaps even all of them) are
        safe to openly disseminate and will be safe to broadly disseminate in the
        future. However, we believe in the future it may be hard to reconcile a culture
        of full open dissemination of frontier AI systems with a culture of societal
        safety.\n\n\n\nIf — and ‘if’ is a key and unresolved point — increasingly
        capable AI models can lead to detrimental effects, or hold the possibility
        of catastrophic accidents, then we’ll need to adjust the norms of what is
        openly disseminated at the frontier.\n\n\n\nSpecifically, we’ll need to ensure
        that AI developers release their systems in a way that provides strong guarantees
        for safety - for example, if we were to discover a meaningful misuse of our
        model, we might put in place classifiers to detect and block attempts to elicit
        that misuse, or we might gate the ability to finetune a system behind a ‘know
        your customer’ rule along with contractual obligations to not finetune towards
        a specific misuse. By comparison, if someone wanted to openly release the
        weights of a model which was capable of the same misuse, they would need to
        both harden the model against that misuse (e.g, via RLHF or RLAIF training)
        and find a way to make this model resilient to attempts to fine-tune it onto
        a dataset that would enable this misuse. We will also need to experiment with
        disclosure processes, similar to how the security community has developed
        norms around pre-notification of disclosures of zero days.\n\n\n\nThough what
        we’ve described is inherently very costly we also believe it is necessary
        - we must do everything we can to avoid AI systems enabling significant misuses
        or causing major accidents. But carrying out any restrictions on the open
        dissemination of AI systems depends on there being broad agreement on what
        unacceptable misuses of AI systems or system behaviors are.\n\n\n\nAnthropic
        is not an impartial actor here - we are a company that primarily develops
        proprietary systems, and we don’t have the legitimacy to make claims here
        about what should or shouldn’t be acceptable in openly disseminated systems.
        Therefore, to resolve questions of open source models we need legitimate third
        parties to develop testing and evaluation approaches that are broadly accepted
        as legitimate, we need these third parties (or other trusted entities) to
        define a narrow and serious set of misuses of AI systems as well as adverse
        AI system behaviors, and we will need to apply these tests to models that
        are both controlled (e.g., via API) or openly disseminated (e.g., via the
        weights being released).\n\n\n\nThird party testing of openly disseminated
        and closed proprietary models can generate the essential information we need
        to understand the safety properties of the AI landscape [2]. If we don’t do
        this, then you could end up in a situation where either a proprietary model
        or openly accessible model directly enables a serious misuse or causes a major
        AI accident - and if that happens, there could be significant harm to people
        and also likely adverse regulations applied to the AI sector.\n\nRegulatory
        capture: Any form of policy can suffer regulatory capture by a sufficiently
        motivated and well-resourced actor: for example, a well-capitalized AI company.
        Some of the ideas we discuss above about openly accessible models are the
        kinds of things which themselves are prone to regulatory capture. It’s important
        that the AI ecosystem remains robust and competitive - AI is a complicated
        field and humanity’s best chance at getting it right likely comes from there
        being a diverse, broad set of actors engaged in its development and oversight.\n\n\n\nWe
        generally advocate for third-party testing and measurement initiatives because
        they seem like the kind of policy infrastructure that helps us to identify
        and prevent concrete harms as well as building capacity that exists independently
        of large companies. Therefore, we think that focusing on the development of
        third-party testing capacity can reduce the risk of regulatory capture and
        create a level playing field for developers. Conversely, industry-led consortia
        might have a tendency to favor approaches that involve high compliance costs
        on the parts of companies regardless of their scale - an approach that inherently
        advantages larger businesses which can spend more money on policy compliance.\n\nWhy
        we’re being careful in what we advocate for in AI policy\n\n\n\nWhen developing
        our policy positions, we assume that regulations tend to create an administrative
        burden both for the party that enforces the regulation (e.g, the government),
        and for the party targeted by the regulation (e.g, AI developers). Therefore,
        we should advocate for policies that are both practical to enforce and feasible
        to comply with. We also note that regulations tend to be accretive - once
        passed, regulations are hard to remove. Therefore, we advocate for what we
        see as the ‘minimal viable policy’ for creating a good AI ecosystem, and we
        will be open to feedback.\n\n\n\nWhy AI policy is important\n\n\n\nThe AI
        systems of today and those of the future are immensely powerful and are capable
        of yielding great benefits to society. We also believe these systems have
        the potential for non-trivial misuses, or could cause accidents if implemented
        poorly. Though the vast majority of our work is technical in nature, we’ve
        come to believe that testing is fundamental to the safety of our systems -
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        of our own models, but also how third-parties can validate claims we make
        about AI systems.\n\n\n\nWe believe that building out a third-party testing
        ecosystem is one of the best ways for bringing more of society into the development
        and oversight of AI systems. We hope that by publishing this post we’ve been
        able to better articulate the benefits of third-party testing as well as outline
        our own position for others to critique and build upon.\n\n\n\nFootnotes\n\n\n\n[1]
        Some countries may also experiment with ‘regulatory markets’ where AI developers
        can buy and sell AI testing services and compete with one another to try to
        build and deploy successively safer, more useful systems.\n\n\n\n[2] For example,
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        could be designed to optimize for a misuse (e.g, phishing or offensive hacking).
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