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AI labs' statements on governance

Published 4 July, 2023, last updated 13 July 2023

This is a collection of some statements on government policy, regulation, and standards from leading AI labs and their leadership. The collected quotes tend to focus on AI safety rather than other governance goals.


Governance of superintelligence (May 2023)

First, we need some degree of coordination among the leading development efforts to ensure that the development of superintelligence occurs in a manner that allows us to both maintain safety and help smooth integration of these systems with society. There are many ways this could be implemented; major governments around the world could set up a project that many current efforts become part of, or we could collectively agree (with the backing power of a new organization like the one suggested below) that the rate of growth in AI capability at the frontier is limited to a certain rate per year.

And of course, individual companies should be held to an extremely high standard of acting responsibly.

Second, we are likely to eventually need something like an IAEA for superintelligence efforts; any effort above a certain capability (or resources like compute) threshold will need to be subject to an international authority that can inspect systems, require audits, test for compliance with safety standards, place restrictions on degrees of deployment and levels of security, etc. Tracking compute and energy usage could go a long way, and give us some hope this idea could actually be implementable. As a first step, companies could voluntarily agree to begin implementing elements of what such an agency might one day require, and as a second, individual countries could implement it. It would be important that such an agency focus on reducing existential risk and not issues that should be left to individual countries, such as defining what an AI should be allowed to say.

Planning for AGI and beyond (Feb 2023)

We think it's important that efforts like ours submit to independent audits before releasing new systems; we will talk about this in more detail later this year. At some point, it may be important to get independent review before starting to train future systems, and for the most advanced efforts to agree to limit the rate of growth of compute used for creating new models. We think public standards about when an AGI effort should stop a training run, decide a model is safe to release, or pull a model from production use are important. Finally, we think it's important that major world governments have insight about training runs above a certain scale.

Altman Senate testimony (May 2023)

Written testimony (before the hearing):

There are several areas I would like to flag where I believe that AI companies and governments can partner productively.

First, it is vital that AI companies–especially those working on the most powerful models–adhere to an appropriate set of safety requirements, including internal and external testing prior to release and publication of evaluation results. To ensure this, the U.S. government should consider a combination of licensing or registration requirements for development and release of AI models above a crucial threshold of capabilities, alongside incentives for full compliance with these requirements.

Second, AI is a complex and rapidly evolving field. It is essential that the safety requirements that AI companies must meet have a governance regime flexible enough to adapt to new technical developments. The U.S. government should consider facilitating multi-stakeholder processes, incorporating input from a broad range of experts and organizations, that can develop and regularly update the appropriate safety standards, evaluation requirements, disclosure practices, and external validation mechanisms for AI systems subject to license or registration.

Third, we are not alone in developing this technology. It will be important for policymakers to consider how to implement licensing regulations on a global scale and ensure international cooperation on AI safety, including examining potential intergovernmental oversight mechanisms and standard-setting.

Questions for the Record (after the hearing):

What are the most important factors for Congress to consider when crafting legislation to regulate artificial intelligence? . . . What specific guardrails and/or regulations do you support that would allow society to benefit from advances in artificial intelligence while minimizing potential risks? [Altman gave identical answers to these two questions]

Any new laws related to AI will become part of a complex legal and policy landscape. A wide range of existing laws already apply to AI, including to our products. And in sectors like medicine, education, and employment, policy stakeholders have already begun to adapt existing laws to take account of the ways that AI impacts those fields. We look forward to contributing to the development of a balanced approach that addresses the risks from AI while also enabling Americans and people around the world to benefit from this technology.

We strongly support efforts to harmonize the emergent accountability expectations for AI, including the efforts of the NIST AI Risk Management Framework, the U.S.-E.U. Trade and Technology Council, and a range of other global initiatives. While these efforts continue to progress, and even before new laws are fully implemented, we see a role for ourselves and other companies to make voluntary commitments on issues such as pre-deployment testing, content provenance, and trust and safety.

We are already doing significant work on responsible and safe approaches to developing and deploying our models, including through red-teaming and quantitative evaluation of potentially dangerous model capabilities and risks. We report on these efforts primarily through a published document that we currently call a System Card. We are refining these approaches in tandem with the broader public policy discussion.

For future generations of the most highly capable foundation models, which are likely to prove more capable than models that have been previously shown to be safe, we support the development of registration, disclosure, and licensing requirements. Such disclosure could help provide policymakers with the necessary visibility to design effective regulatory solutions, and get ahead of trends at the frontier of AI progress. To be beneficial and not create new risks, it is crucial that any such regimes prioritize the security of the information disclosed. Licensure is common in safety-critical and other high-risk contexts, such as air travel, power generation, drug manufacturing, and banking. Licensees could be required to perform pre-deployment risk assessments and adopt state-of-the-art security and deployment safeguards.

. . .

During the hearing, you testified that “a new framework” is necessary for imposing liability for harms caused by artificial intelligence—separate from Section 230 of the Communications Decency Act—and offered to “work together” to develop this framework. What features do you consider most important for a liability framework for artificial Intelligence?

Any new framework should apportion responsibility in such a way that AI services, companies who build on AI services, and users themselves appropriately share responsibility for the choices that they each control and can make, and have appropriate incentives to take steps to avoid harm.

OpenAI disallows the use of our models and tools for certain activities and content, as outlined in our usage policies. These policies are designed to prohibit the use of our models and tools in ways that may cause individual or societal harm. We update these policies in response to new risks and updated information about how our models are being used. Access to and use of our models are also subject to OpenAI's Terms of Use which, among other things, prohibit the use of our services to harm people's rights, and prohibit presenting output from our services as being human-generated when it was not.

One important consideration for any liability framework is the level of discretion that should be granted to companies like OpenAI, and people who develop services using these technologies, in determining the level of freedom granted to users. If liability frameworks are overly restrictive, the capabilities that are offered to users could in turn be heavily censored or restricted, leading to potentially stifling outcomes and negative implications for many of the beneficial capabilities of AI, including free speech and education. However, if liability frameworks are too lax, negative externalities may appear where a company benefits from lack of oversight and regulation at the expense of the overall good of society. One of the critical features of any liability framework is to attempt to find and continually refine this balance.

Given these realities, it would be helpful for an assignment of rights and responsibilities related to harms to recognize that the results of AI systems are not solely determined by these systems, but instead respond to human-driven commands. For example, a framework should take into account the degree to which each actor in the chain of events that resulted in the harm took deliberate actions, such as whether a developer clearly stipulated allowed/disallowed usages or developed reasonable safeguards, and whether a user disregarded usage rules or acted to overcome such safeguards.

AI services should also be encouraged to ensure a baseline of safety and risk disclosures for our products to minimize potential harm. This thinking underlies our approach of putting our systems through safety training and testing prior to release, frank disclosures of risk and mitigations, and enforcement against misuse. Care should be taken to ensure that liability frameworks do not inadvertently create unintended incentives for AI providers to reduce the scope or visibility of such disclosures.

Furthermore, many of the highest-impact uses of new AI tools are likely to take place in specific sectors that are already covered by sector-specific laws and regulations, such as health, financial services and education. Any new liability regime should take into consideration the extent to which existing frameworks could be applied to AI technologies as an interpretive matter. To the extent new or additional rules are needed, they would need to be harmonized with these existing laws.

Hearing transcript:

[Blumenthal asked Altman “the effect on jobs . . . is really my biggest nightmare in the long term. Let me ask you what your biggest nightmare is, and whether you share that concern.” His reply only mentioned jobs. Marcus noted that “Sam's worst fear I do not think is employment. And he never told us what his worst fear actually is. And I think it's germane to find out.” Altman vaguely replied about “significant harm to the world.”]

. . .

I think the US should lead here and do things first, but to be effective we do need something global. . . . There is precedent–I know it sounds naive to call for something like this, and it sounds really hard–there is precedent. We've done it before with the IAEA. We've talked about doing it for other technologies. Given what it takes to make these models–the chip supply chain, the limited number of competitive GPUs, the power the US has over these companies–I think there are paths to the US setting some international standards that other countries would need to collaborate with and be part of that are actually workable, even though it sounds on its face like an impractical idea. And I think it would be great for the world.

. . .

Do you agree with me that the simplest way and the most effective way [to implement licensing of AI tools] is to have an agency that is more nimble and smarter than Congress . . . [overseeing] what you do?
We'd be enthusiastic about that.

. . .

I would like you to assume there is likely a berserk wing of the artificial intelligence community that intentionally or unintentionally could use artificial intelligence to kill all of us and hurt us the entire time that we are dying. . . . Please tell me in plain English, two or three reforms, regulations, if any, that you would, you would implement if you were queen or king for a day.

Number one, I would form a new agency that licenses any effort above a certain scale of capabilities and can take that license away and ensure compliance with safety standards. Number two, I would create a set of safety standards . . . as the dangerous capability evaluations. One example that we've used in the past is looking to see if a model can self-replicate and self-exfiltrate into the wild. We can give your office a long other list of the things that we think are important there, but specific tests that a model has to pass before it can be deployed into the world. And then third I would require independent audits. So not just from the company or the agency, but experts who can say the model is or isn't compliance with these stated safety thresholds and these percentages of performance on question X or Y.

. . .

I'm a believer in defense in depth. I think that there should be limits on what a deployed model is capable of, and then what it actually does too.

. . .

Would you pause any further development for six months or longer?

So first of all, after we finished training GPT-4, we waited more than six months to deploy it. We are not currently training what will be GPT-5. We don't have plans to do it in the next six months. But I think the frame of the letter is wrong. What matters is audits, red teaming, safety standards that a model needs to pass before training. If we pause for six months, then I'm not really sure what we do then– do we pause for another six? Do we kind of come up with some rules then? The standards that we have developed and that we've used for GPT-4 deployment, we want want to build on those, but we think that's the right direction, not a calendar clock pause. There may be times–I expect there will be times–when we find something that we don't understand and we really do need to take a pause, but we don't see that yet. Nevermind all the benefits.

You don't see what yet? You're comfortable with all of the potential ramifications from the current existing technology?

I'm sorry. We don't see the reasons to not train a new one. For deploying, as I mentioned, I think there's all sorts of risky behavior and there's limits we put, we have to pull things back sometimes, add new ones. I meant we don't see something that would stop us from training the next model, where we'd be so worried that we'd create something dangerous even in that process, let alone the deployment that would happen.

NTIA comment (Jun 2023)

OpenAI's Current Approaches

We are refining our practices in tandem with the evolving broader public conversation. Here we provide details on several aspects of our approach.

System Cards

Transparency is an important element of building accountable AI systems. A key part of our approach to accountability is publishing a document that we currently call a System Card, for new AI systems that we deploy. Our approach draws inspiration from previous research work on model cards and system cards. To date, OpenAI has published two system cards: the GPT-4 System Card and DALL-E 2 System Card.

We believe that in most cases, it is important for these documents to analyze and describe the impacts of a system – rather than focusing solely on the model itself – because a system's impacts depend in part on factors other than the model, including use case, context, and real world interactions. Likewise, an AI system's impacts depend on risk mitigations such as use policies, access controls, and monitoring for abuse. We believe it is reasonable for external stakeholders to expect information on these topics, and to have the opportunity to understand our approach.

Our System Cards aim to inform readers about key factors impacting the system's behavior, especially in areas pertinent for responsible usage. We have found that the value of System Cards and similar documents stems not only from the overview of model performance issues they provide, but also from the illustrative examples they offer. Such examples can give users and developers a more grounded understanding of the described system's performance and risks, and of the steps we take to mitigate those risks. Preparation of these documents also helps shape our internal practices, and illustrates those practices for others seeking ways to operationalize responsible approaches to AI.

Qualitative Model Evaluations via Red Teaming

Red teaming is the process of qualitatively testing our models and systems in a variety of domains to create a more holistic view of the safety profile of our models. We conduct red-teaming internally with our own staff as part of model development, as well as with people who operate independently of the team that builds the system being tested. In addition to probing our organization's capabilities and resilience to attacks, red teams also use stress testing and boundary testing methods, which focus on surfacing edge cases and other potential failure modes with potential to cause harm.

Red teaming is complementary to automated, quantitative evaluations of model capabilities and risks that we also conduct, which we describe in the next section. It can shed light on risks that are not yet quantifiable, or those for which more standardized evaluations have not yet been developed. Our prior work on red teaming is described in the DALL-E 2 System Card and the GPT-4 System Card.

Our red teaming and testing is generally conducted during the development phase of a new model or system. Separately from our own internal testing, we recruit testers outside of OpenAI and provide them with early access to a system that is under development. Testers are selected by OpenAI based on prior work in the domains of interest (research or practical expertise), and have tended to be a combination of academic researchers and industry professionals (e.g, people with work experience in Trust & Safety settings). We evaluate and validate results of these tests, and take steps to make adjustments and deploy mitigations where appropriate.

OpenAI continues to take steps to improve the quality, diversity, and experience of external testers for ongoing and future assessments.

Quantitative Model Evaluations

In addition to the qualitative red teaming described above, we create automated, quantitative evaluations for various capabilities and safety oriented risks, including risks that we find via methods like red teaming. These evaluations allow us to compare different versions of our models with each other, iterate on research methodologies that improve safety, and ultimately act as an input into decision-making about which model versions we choose to deploy. Existing evaluations span topics such as erotic content, hateful content, and content related to self-harm among others, and measure the propensity of the models to generate such content.

Usage Policies

OpenAI disallows the use of our models and tools for certain activities and content, as outlined in our usage policies. These policies are designed to prohibit the use of our models and tools in ways that cause individual or societal harm. We update these policies in response to new risks and updated information about how our models are being used. Access to and use of our models are also subject to OpenAI's Terms of Use which, among other things, prohibit the use of our services to harm people's rights, and prohibit presenting output from our services as being human-generated when it was not.

We take steps to limit the use of our models for harmful activities by teaching models to refuse to respond to certain types of requests that may lead to potentially harmful responses. In addition, we use a mix of reviewers and automated systems to identify and take action against misuse of our models. Our automated systems include a suite of machine learning and rule-based classifier detections designed to identify content that might violate our policies. When a user repeatedly prompts our models with policy-violating content, we take actions such as issuing a warning, temporarily suspending the user, or in severe cases, banning the user.

Open Challenges in AI Accountability

As discussed in the RFC, there are many important questions related to AI Accountability that are not yet resolved. In the sections that follow, we provide additional perspective on several of these questions.

Assessing Potentially Dangerous Capabilities

Highly capable foundation models have both beneficial capabilities, as well as the potential to cause harm. As the capabilities of these models get more advanced, so do the scale and severity of the risks they may pose, particularly if under direction from a malicious actor or if the model is not properly aligned with human values.

Rigorously measuring advances in potentially dangerous capabilities is essential for effectively assessing and managing risk. We are addressing this by exploring and building evaluations for potentially dangerous capabilities that range from simple, scalable, and automated tools to bespoke, intensive evaluations performed by human experts. We are collaborating with academic and industry experts, and ultimately aim to contribute to the development of a diverse suite of evaluations that can contribute to the formation of best practices for assessing emerging risks in highly capable foundation models. We believe dangerous capability evaluations are an increasingly important building block for accountability and governance in frontier AI development.

Open Questions About Independent Assessments

Independent assessments of models and systems, including by third parties, may be increasingly valuable as model capabilities continue to increase. Such assessments can strengthen accountability and transparency about the behaviors and risks of AI systems.

Some forms of assessment can occur within a single organization, such as when a team assesses its own work or when a team or part of the organization produces a model and another team or part, acting independently, tests that model. A different approach is to have an external third party conduct an assessment. As described above, we currently rely on a mixture of internal and external evaluations of our models.

Third-party assessments may focus on specific deployments, a model or system at some moment in time, organizational governance and risk management practices, specific applications of a model or system, or some combination thereof. The thinking and potential frameworks to be used in such assessments continue to evolve rapidly, and we are monitoring and considering our own approach to assessments.

For any third-party assessment, the process of selecting auditors/assessors with appropriate expertise and incentive structures would benefit from further clarity. In addition, selecting the appropriate expectations against which to assess organizations or models is an open area of exploration that will require inputs from different stakeholders. Finally, it will be important for assessments to consider how systems might evolve over time and build that into the process of an assessment / audit.

Registration and Licensing for Highly Capable Foundation Models

We support the development of registration and licensing requirements for future generations of the most highly capable foundation models. Such models may have sufficiently dangerous capabilities to pose significant risks to public safety; if they do, we believe they should be subject to commensurate accountability requirements.

It could be appropriate to consider disclosure and registration expectations for training processes that are expected to produce highly capable foundation models. Such disclosure could help enable policymakers with the necessary visibility to design effective regulatory solutions, and get ahead of trends at the frontier of AI progress. It is crucial that any such regimes prioritize the security of the information disclosed.

AI developers could be required to receive a license to create highly capable foundation models which are likely to prove more capable than models previously shown to be safe. Licensure is common in safety-critical and other high-risk contexts, such as air travel, power generation, drug manufacturing, and banking. Licensees could be required to perform pre-deployment risk assessments and adopt state-of-the-art security and deployment safeguards; indeed, many of the accountability practices that the NTIA will be considering could be appropriate licensure requirements. Introducing licensure requirements at the computing provider level could also be a powerful complementary tool for enforcement.

There remain many open questions in the design of registration and licensing mechanisms for achieving accountability at the frontier of AI development. We look forward to collaborating with policymakers in addressing these questions.

Altman interview (Bloomberg, Jun 2023)

What do you think about the certification system of AI models that the Biden administration has proposed?

I think there's some version of that that's really good. I think that people training models that are way above– any model scale that we have today, but above some certain capability threshold– I think you should need to go through a certification process for that. I think there should be external audits and safety tests.

Frontier AI regulation (Jul 2023)

Note: some authors are affiliated with OpenAI, including Jade Leung and Miles Brundage, two governance leads. Some authors are affiliated with Google DeepMind. This paper is listed under OpenAI since OpenAI includes it on their Research page. It's not clear how much OpenAI endorses it.

Self-regulation is unlikely to provide sufficient protection against the risks from frontier AI models: government intervention will be needed. We explore options for such intervention. These include:

* Mechanisms to create and update safety standards for responsible frontier AI development and deployment. These should be developed via multi-stakeholder processes, and could include standards relevant to foundation models overall, not exclusive to frontier AI. These processes should facilitate rapid iteration to keep pace with the technology.
* Mechanisms to give regulators visibility into frontier AI development, such as disclosure regimes, monitoring processes, and whistleblower protections. These equip regulators with the information needed to address the appropriate regulatory targets and design effective tools for governing frontier AI. The information provided would pertain to qualifying frontier AI development processes, models, and applications.
* Mechanisms to ensure compliance with safety standards. Self-regulatory efforts, such as voluntary certification, may go some way toward ensuring compliance with safety standards by frontier AI model developers. However, this seems likely to be insufficient without government intervention, for example by empowering a supervisory authority to identify and sanction non-compliance; or by licensing the deployment and potentially the development of frontier AI. Designing these regimes to be well-balanced is a difficult challenge; we should be sensitive to the risks of overregulation and stymieing innovation on the one hand, and moving too slowly relative to the pace of AI progress on the other.

Next, we describe an initial set of safety standards that, if adopted, would provide some guardrails on the development and deployment of frontier AI models. Versions of these could also be adopted for current AI models to guard against a range of risks. We suggest that at minimum, safety standards for frontier AI development should include:

* Conducting thorough risk assessments informed by evaluations of dangerous capabilities and controllability. This would reduce the risk that deployed models possess unknown dangerous capabilities, or behave unpredictably and unreliably.
* Engaging external experts to apply independent scrutiny to models. External scrutiny of the safety and risk profile of models would both improve assessment rigor and foster accountability to the public interest.
* Following standardized protocols for how frontier AI models can be deployed based on their assessed risk. The results from risk assessments should determine whether and how the model is deployed, and what safeguards are put in place. This could range from deploying the model without restriction to not deploying it at all. In many cases, an intermediate option—deployment with appropriate safeguards (e.g., more post-training that makes the model more likely to avoid risky instructions)—may be appropriate.
* Monitoring and responding to new information on model capabilities. The assessed risk of deployed frontier AI models may change over time due to new information, and new post-deployment enhancement techniques. If significant information on model capabilities is discovered post-deployment, risk assessments should be repeated, and deployment safeguards updated.

Going forward, frontier AI models seem likely to warrant safety standards more stringent than those imposed on most other AI models, given the prospective risks they pose. Examples of such standards include: avoiding large jumps in capabilities between model generations; adopting state-of-the-art alignment techniques; and conducting pre-training risk assessments. Such practices are nascent today, and need further development.

Altman interview (NYmag, Mar 2023)

I think the thing that I would like to see happen immediately is just much more insight into what companies like ours are doing, companies that are training above a certain level of capability at a minimum. A thing that I think could happen now is the government should just have insight into the capabilities of our latest stuff, released or not, what our internal audit procedures and external audits we use look like, how we collect our data, how we're red-teaming these systems, what we expect to happen, which we may be totally wrong about. [“What I mean is government auditors sitting in our buildings.”] We could hit a wall anytime, but our internal road-map documents, when we start a big training run, I think there could be government insight into that. And then if that can start now– I do think good regulation takes a long time to develop. It's a real process. They can figure out how they want to have oversight. . . .

Those efforts probably do need a new regulatory effort, and I think it needs to be a global regulatory body. And then people who are using AI, like we talked about, as a medical adviser, I think the FDA can give probably very great medical regulation, but they'll have to update it for the inclusion of AI. But I would say creation of the systems and having something like an IAEA that regulates that is one thing, and then having existing industry regulators still do their regulation [Ed: he was cut off] . . . .

Section 230 doesn't seem to cover generative AI. Is that a problem?

I think we will need a new law for use of this stuff, and I think the liability will need to have a few different frameworks. If someone is tweaking the models themselves, I think it's going to have to be the last person who touches it has the liability, and that's —

But it's not full immunity that the platform's getting —

I don't think we should have full immunity. Now, that said, I understand why you want limits on it, why you do want companies to be able to experiment with this, you want users to be able to get the experience they want, but the idea of no one having any limits for generative AI, for AI in general, that feels super-wrong.

Brockman House testimony (Jun 2018)

Written testimony:

Policy recommendations

1. Measurement. Many other established voices in the field have tried to combat panic about AGI by instead saying it not something to worry about or is unfathomably far off. We recommend neither panic nor a lack of caution. Instead, we recommend investing more resources into understanding where the field is, how quickly progress is accelerating, and what roadblocks might lie ahead. We’re exploring this problem via our own research and support of initiatives like the AI Index. But there’s much work to be done, and we are available to work with governments around the world to support their own measurement and assessment initiatives — for instance, we participated in a GAO-led study on AI last year.

2. Foundation for international coordination. AGI’s impact, like that of the Internet before it, won’t track national boundaries. Successfully using AGI to make the world better for people, while simultaneously preventing rogue actors from abusing it, will require international coordination of some form. Policymakers today should invest in creating the foundations for successful international coordination in AI, and recognize that the more adversarial the climate in which AGI is created, the less likely we are to achieve a good outcome. We think the most practical place to start is actually with the measurement initiatives: each government working on measurement will create teams of people who have a strong motivation to talk to their international counterparts to harmonize measurement schemes and develop global standards.

Brockman Senate testimony (Nov 2016)


Charting a Path to AI Accountability (Jun 2023)

Anthropic's NTIA comment is a longer version of this blogpost.

There is currently no robust and comprehensive process for evaluating today's advanced artificial intelligence (AI) systems, let alone the more capable systems of the future. Our submission presents our perspective on the processes and infrastructure needed to ensure AI accountability. Our recommendations consider the NTIA's potential role as a coordinating body that sets standards in collaboration with other government agencies like the National Institute of Standards and Technology (NIST).

In our recommendations, we focus on accountability mechanisms suitable for highly capable and general-purpose AI models. Specifically, we recommend:

Fund research to build better evaluations

Increase funding for AI model evaluation research. Developing rigorous, standardized evaluations is difficult and time-consuming work that requires significant resources. Increased funding, especially from government agencies, could help drive progress in this critical area.

Require companies in the near-term to disclose evaluation methods and results. Companies deploying AI systems should be mandated to satisfy some disclosure requirements with regard to their evaluations, though these requirements need not be made public if doing so would compromise intellectual property (IP) or confidential information. This transparency could help researchers and policymakers better understand where existing evaluations may be lacking.

Develop in the long term a set of industry evaluation standards and best practices. Government agencies like NIST could work to establish standards and benchmarks for evaluating AI models' capabilities, limitations, and risks that companies would comply with.

Create risk-responsive assessments based on model capabilities
Develop standard capabilities evaluations for AI systems. Governments should fund and participate in the development of rigorous capability and safety evaluations targeted at critical risks from advanced AI, such as deception and autonomy. These evaluations can provide an evidence-based foundation for proportionate, risk-responsive regulation.

Develop a risk threshold through more research and funding into safety evaluations. Once a risk threshold has been established, we can mandate evaluations for all models against this threshold.

* If a model falls below this risk threshold, existing safety standards are likely sufficient. Verify compliance and deploy.
* If a model exceeds the risk threshold and safety assessments and mitigations are insufficient, halt deployment, significantly strengthen oversight, and notify regulators. Determine appropriate safeguards before allowing deployment.
Establish pre-registration for large AI training runs

Establish a process for AI developers to report large training runs ensuring that regulators are aware of potential risks. This involves determining the appropriate recipient, required information, and appropriate cybersecurity, confidentiality, IP, and privacy safeguards.

Establish a confidential registry for AI developers conducting large training runs to pre-register model details with their home country's national government (e.g., model specifications, model type, compute infrastructure, intended training completion date, and safety plans) before training commences. Aggregated registry data should be protected to the highest available standards and specifications.

Empower third party auditors that are…

Technically literate – at least some auditors will need deep machine learning experience;

Security-conscious – well-positioned to protect valuable IP, which could pose a national security threat if stolen; and

Flexible – able to conduct robust but lightweight assessments that catch threats without undermining US competitiveness.

Mandate external red teaming before model release

Mandate external red teaming for AI systems, either through a centralized third party (e.g., NIST) or in a decentralized manner (e.g., via researcher API access) to standardize adversarial testing of AI systems. This should be a precondition for developers who are releasing advanced AI systems.

Establish high-quality external red teaming options before they become a precondition for model release. This is critical as red teaming talent currently resides almost exclusively within private AI labs.

Advance interpretability research

Increase funding for interpretability research. Provide government grants and incentives for interpretability work at universities, nonprofits, and companies. This would allow meaningful work to be done on smaller models, enabling progress outside frontier labs.

Recognize that regulations demanding interpretable models would currently be infeasible to meet, but may be possible in the future pending research advances.

Enable industry collaboration on AI safety via clarity around antitrust
Regulators should issue guidance on permissible AI industry safety coordination given current antitrust laws. Clarifying how private companies can work together in the public interest without violating antitrust laws would mitigate legal uncertainty and advance shared goals.

We believe this set of recommendations will bring us meaningfully closer to establishing an effective framework for AI accountability. Doing so will require collaboration between researchers, AI labs, regulators, auditors, and other stakeholders. Anthropic is committed to supporting efforts to enable the safe development and deployment of AI systems. Evaluations, red teaming, standards, interpretability and other safety research, auditing, and strong cybersecurity practices are all promising avenues for mitigating the risks of AI while realizing its benefits.

We believe that AI could have transformative effects in our lifetime and we want to ensure that these effects are positive. The creation of robust AI accountability and auditing mechanisms will be vital to realizing this goal.

[Expand NIST] (Apr 2023)

This is a policy memo; there is also a corresponding blogpost. It follows up on the following source. It also succeeds Clark Senate testimony (Sep 2022).

With this additional resourcing, NIST could continue and expand its work on AI assurance efforts like:

* Cataloging existing AI evaluations and benchmarks used in industry and academia
* Investigating the scientific validity of existing evaluations (e.g., adherence to quality control practices, effects of technical implementation choices on evaluation results, etc.)
* Designing novel evaluations that address limitations of existing evaluations
* Developing technical standards for how to identify vulnerabilities in open-ended systems
* Developing disclosure standards to enhance transparency around complex AI systems
* Partnering with allies on international standards to promote multilateral interoperability
* Further developing and updating the AI Risk Management Framework

More resourcing will allow NIST to build out much-needed testing environments for today's generative AI systems.

Comment on "Study To Advance a More Productive Tech Economy" (Feb 2022)

Followed up on by the preceding source.

The past decade of AI development charts a future course of increasingly large, high performing industry models that can be adapted for a wide variety of applications. Without intervention or investment however, we risk a future where AI development and oversight is controlled by a handful of actors, motivated primarily by commercial priorities. To ensure these systems drive a more productive and broadly beneficial economy, we must expand access and representation in their creation and evaluation.

A robust assurance ecosystem would help increase public confidence in AI technology, enable a more competitive R&D environment, and foster a stronger U.S. economy.

The federal government can support this by:

* Increasing funding for academic researchers to access compute resources through efforts such as the National AI Research Resource (NAIRR) and the University Technology Center Program proposed in the United States Innovation and Competition Act (USICA)
* Providing financial grants to researchers, especially those currently underrepresented, who are developing assurance indicators in areas such as bias and fairness or novel forms of AI system oversight
* Prioritizing the development of AI testbeds, centralized datasets, and standardized testing protocols
* Identifying evaluations created by independent researchers and creating a catalog of validated tests
* Standardizing the essential components of self-designed evaluations and establishing norms for how evaluation results should be disclosed

Google DeepMind

NTIA comment (Google and Google DeepMind, Jun 2023)

While it is tempting to look for silver-bullet policy solutions, AI raises complex questions that require nuanced answers. It is a 21st century technology that requires a 21st century governance model. We need a multi-layered, multi-stakeholder approach to AI governance. This will include:
* Industry, civil society, and academic experts developing and sharing best practices and technical standards for responsible AI, including around safety and misinformation issues;
* A hub-and-spoke model of national regulation; and
* International coordination among allies and partners, including around geopolitical

security and competitiveness and alignment on regulatory approaches.

At the national level, we support a hub-and-spoke approach—with a central agency like the National Institute of Standards and Technology (NIST) informing sectoral regulators overseeing AI implementation—rather than a “Department of AI.” AI will present unique issues in financial services, health care, and other regulated industries and issue areas that will benefit from the expertise of regulators with experience in those sectors—which works beer than a new regulatory agency promulgating and implementing upstream rules that are not adaptable to the diverse contexts in which AI is deployed.

Maximizing the economic opportunity from AI will also require a joint effort across federal, state, and local governments, the private sector, and civil society to equip workers to harness AI-driven tools. AI is likely to generate significant economy-wide benets. At the same time, to mitigate displacement risks, the private sector will need to develop proof-of-concept efforts on skilling, training, and continuing education, while the public sector can help validate and scale these efforts to ensure workers have wrap-around support. Smart deployment of AI coupled with thoughtful policy choices and an adaptive safety net can ensure that AI ultimately leads to higher wages and better living standards.

With respect to U.S. regulation to promote accountability, we urge policymakers to:

* Promote enabling legislation for AI innovation leadership. Federal policymakers can eliminate legal barriers to AI accountability efforts, including by establishing competition safe harbors for open public-private and cross-industry collaboration on AI safety research, and clarifying the liability for misuse and abuse of AI systems by different users (e.g., researchers, authors, creators of AI systems, implementers, and end users). Policymakers should also consider related legal frameworks that support innovation, such as adopting a uniform national privacy law that protects personal information and an AI model's incidental use of publicly available information.
* Support proportionate, risk-based accountability measures. Deployers of high-risk AI systems should provide documentation about their systems and undergo independent risk assessments focused on specific applications.
* Regulate under a “hub-and-spoke” model rather than creating a new AI regulator. Under this model, regulators across the government would engage a central, coordinating agency with AI expertise, such as NIST, with Oce of Management and Budget (OMB) support, for technical guidance on best practices on AI accountability.
* Use existing authorities to expedite governance and align AI and traditional rules. Where appropriate, sectoral regulators would provide updates clarifying how existing authorities apply to the use of AI systems, as well as how organizations can demonstrate compliance of an AI system with these existing regulations.
* Assign to AI deployers the responsibility of assessing the risk of their unique deployments, auditing, and other accountability mechanisms as a result of their unparalleled awareness of their specific uses and related risks of the AI system.
* Define appropriate accountability metrics and benchmarks, as well as terms that may be ambiguous, to guide compliance. Recognize that many existing systems are imperfect and that even imperfect AI systems may, in some settings, be able to improve service levels, reduce costs, or increase affordability and availability.
* Consider the tradeoffs between different policy objectives, including efficiency and productivity enhancements, transparency, fairness, privacy, security, and resilience.
* Design regulation to promote competitiveness, responsible innovation, and broad access to the economic benefits of AI.
* Require high standards of cybersecurity protections (including access controls) and develop targeted “next-generation” trade control policies.
* Avoid requiring disclosures that include trade secrets or confidential information (potentially advantaging adversaries) or stymie this innovative sector as it continues to evolve.
* Prepare the American workforce for AI-driven job transitions and promote opportunities to broadly share AI's benets.

Finally, NTIA asks how policymakers can otherwise advance AI accountability. The U.S. government should:

* Continue building technical and human capacity into the ecosystem to enable effective risk management. The government should deepen investment in fundamental responsible AI research (including bias and human-centered systems design) through federal agency initiatives, research centers, and foundations, as well as by creating and supporting public-private partnerships.
* Drive international policy alignment, working with allies and partners to develop common approaches that reflect democratic values. Policymakers can support common standards and frameworks that enable interoperability and harmonize global AI governance approaches. This can be done by: (1) enabling trusted data flows across national borders, (2) establishing multinational AI research resources, (3) encouraging the adoption of common approaches to AI regulation and governance and a common lexicon, based on the work of the Organisation for Economic Co-operation and Development (OECD), (4) working within standard-setting bodies such as the International Organization for Standardization (ISO) and the Institute of Electrical and Electronics Engineers (IEEE) to establish rules, benchmarks, and governance mechanisms that can serve as a baseline for domestic regulatory approaches and deter regulatory fragmentation, (5) using trade and economic agreements to support the development of consistent and non-discriminatory AI regulations, (6) promoting copyright systems that enable appropriate and fair use of copyrighted content to enable the training of AI models, while supporting workable opt-outs for websites, and (7) establishing more effective mechanisms for information and best-practice sharing among allies and between the private and the public sectors.
* Explore updating procurement rules to incentivize AI accountability, and ensure OMB and the Federal Acquisition Regulatory Council are engaged in any such updates. It will be critical for agencies who are further ahead in their development of AI procurement practices to remain coordinated and aligned upon a common baseline to effectively scale responsible governance (e.g., through the NIST AI Risk Management Framework (AI RMF)).

The United States currently leads the world in AI development, and with the right policies that support both trustworthy AI and innovation, the United States can continue to lead and help allies enhance their own competitiveness while aligning around a positive and responsible vision for AI. Centering policies around economic opportunity, promoting responsibility and trust, and furthering our collective security will advance today's and tomorrow's AI innovation and unleash benets across society.

Exploring institutions for global AI governance (Jul 2023)

Note: this is a Google DeepMind blogpost about the paper International Institutions for Advanced AI. Some authors of the paper are affiliated with Google DeepMind. One author is affiliated with OpenAI. It's not clear how much Google DeepMind endorses it.

We explore four complementary institutional models to support global coordination and governance functions:

* An intergovernmental Commission on Frontier AI could build international consensus on opportunities and risks from advanced AI and how they may be managed. This would increase public awareness and understanding of AI prospects and issues, contribute to a scientifically informed account of AI use and risk mitigation, and be a source of expertise for policymakers.
* An intergovernmental or multi-stakeholder Advanced AI Governance Organisation could help internationalise and align efforts to address global risks from advanced AI systems by setting governance norms and standards and assisting in their implementation. It may also perform compliance monitoring functions for any international governance regime.
* A Frontier AI Collaborative could promote access to advanced AI as an international public-private partnership. In doing so, it would help underserved societies benefit from cutting-edge AI technology and promote international access to AI technology for safety and governance objectives.
* An AI Safety Project could bring together leading researchers and engineers, and provide them with access to computation resources and advanced AI models for research into technical mitigations of AI risks. This would promote AI safety research and development by increasing its scale, resourcing, and coordination.

Hassabis interview (Klein, Jul 2023)

If we're getting to a point where somebody is getting near something like a general intelligence system, is that too powerful a technology to be in private hands? Should this be something that whichever corporate entity gets there first controls? Or do we need something else to govern it?

My personal view is that this is such a big thing in its fullness of time. I think it's bigger than any one corporation or even one nation. I think it needs international cooperation. I've often talked in the past about a CERN-like effort for A.G.I., and I quite like to see something like that as we get closer, maybe in many years from now, to an A.G.I. system, where really careful research is done on the safety side of things, understanding what these systems can do, and maybe testing them in controlled conditions, like simulations or games first, like sandboxes, very robust sandboxes with lots of cybersecurity protection around them. I think that would be a good way forward as we get closer towards human-level A.I. systems.

Primary Author: Zach Stein-Perlman

uncategorized/ai_labs_statements_on_governance.txt · Last modified: 2023/07/14 19:03 by harlanstewart