[Federal Register Volume 89, Number 38 (Monday, February 26, 2024)]
[Notices]
[Pages 14059-14063]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2024-03763]


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DEPARTMENT OF COMMERCE

National Telecommunications and Information Administration

[Docket No. 240216-0052]
RIN 0660-XC060


Dual Use Foundation Artificial Intelligence Models With Widely 
Available Model Weights

AGENCY: National Telecommunications and Information Administration, 
Department of Commerce.

ACTION: Notice; request for comment.

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SUMMARY: On October 30, 2023, President Biden issued an Executive order 
on ``Safe, Secure, and Trustworthy Development and Use of Artificial 
Intelligence,'' which directed the Secretary of Commerce, acting 
through the Assistant Secretary of Commerce for Communications and 
Information, and in consultation with the Secretary of State, to 
conduct a public consultation process and issue a report on the 
potential risks, benefits, other implications, and appropriate policy 
and regulatory approaches to dual-use foundation models for which the 
model weights are widely available. Pursuant to that Executive order, 
the National Telecommunications and Information Administration (NTIA) 
hereby issues this Request for Comment on these issues. Responses 
received will be used to submit a report to the President on the 
potential benefits, risks, and implications of dual-use foundation

[[Page 14060]]

models for which the model weights are widely available, as well as 
policy and regulatory recommendations pertaining to those models.

DATES: Written comments must be received on or before March 27, 2024.

ADDRESSES: All electronic public comments on this action, identified by 
Regulations.gov docket number NTIA-2023-0009, may be submitted through 
the Federal e-Rulemaking Portal at https://www.regulations.gov. The 
docket established for this request for comment can be found at 
www.Regulations.gov, NTIA-2023-0009. To make a submission, click the 
``Comment Now!'' icon, complete the required fields, and enter or 
attach your comments. Additional instructions can be found in the 
``Instructions'' section below, after SUPPLEMENTARY INFORMATION.

FOR FURTHER INFORMATION CONTACT: Please direct questions regarding this 
Request for Comment to Travis Hall at [email protected] with ``Openness in 
AI Request for Comment'' in the subject line. If submitting comments by 
U.S. mail, please address questions to Bertram Lee, National 
Telecommunications and Information Administration, U.S. Department of 
Commerce, 1401 Constitution Avenue NW, Washington, DC 20230. Questions 
submitted via telephone should be directed to (202) 482-3522. Please 
direct media inquiries to NTIA's Office of Public Affairs, telephone: 
(202) 482-7002; email: [email protected].

SUPPLEMENTARY INFORMATION:

Background and Authority

    Artificial intelligence (AI) \1\ has had, and will have, a 
significant effect on society, the economy, and scientific progress. 
Many of the most prominent models, including the model that powers 
ChatGPT, are ``fully closed'' or ``highly restricted,'' with limited or 
no public access to their inner workings. The recent introduction of 
large, publicly-available models, such as those from Google, Meta, 
Stability AI, Mistral, the Allen Institute for AI, and EleutherAI, 
however, has fostered an ecosystem of increasingly ``open'' advanced AI 
models, allowing developers and others to fine-tune models using widely 
available computing.\2\
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    \1\ Artificial Intelligence (AI) ``has the meaning set forth in 
15 U.S.C. 9401(3): a machine-based system that can, for a given set 
of human-defined objectives, make predictions, recommendations, or 
decisions influencing real or virtual environments. Artificial 
intelligence systems use machine- and human-based inputs to perceive 
real and virtual environments; abstract such perceptions into models 
through analysis in an automated manner; and use model inference to 
formulate options for information or action.'' see Executive Office 
of the President, Safe, Secure, and Trustworthy Development and Use 
of Artificial Intelligence, 88 FR 75191 (November 1, 2023) https://www.federalregister.gov/documents/2023/11/01/2023-24283/safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence. ``AI 
Model'' means ``a component of an information system that implements 
AI technology and uses computational, statistical, or machine-
learning techniques to produce outputs from a given set of inputs.'' 
see Id.
    \2\ See e.g., Zoe Brammer, How Does Access Impact Risk? 
Assessing AI Foundation Model Risk Along a Gradient of Access, The 
Institute for Security and Technology (December 2023) https://securityandtechnology.org/wp-content/uploads/2023/12/How-Does-Access-Impact-Risk-Assessing-AI-Foundation-Model-Risk-Along-A-Gradient-of-Access-Dec-2023.pdf; Irene Solaiman, The Gradient of 
Generative AI Release: Methods and Considerations, 
arXiv:2302.04844v1 (February 5, 2023); https://arxiv.org/pdf/2302.04844.pdf.
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    Dual use foundation models with widely available weights (referred 
to here as open foundation models) could play a key role in fostering 
growth among less resourced actors, helping to widely share access to 
AI's benefits.\3\ Small businesses, academic institutions, underfunded 
entrepreneurs, and even legacy businesses have used these models to 
further innovate, advance scientific knowledge, and gain potential 
competitive advantages in the marketplace. The concentration of access 
to foundation models into a small subset of organizations poses the 
risk of hindering such innovation and advancements, a concern that 
could be lessened by availability of open foundation models. Open 
foundation models can be readily adapted and fine-tuned to specific 
tasks and possibly make it easier for system developers to scrutinize 
the role foundation models play in larger AI systems, which is 
important for rights- and safety-impacting AI systems (e.g. healthcare, 
education, housing, criminal justice, online platforms etc.).\4\ These 
open foundation models have the potential to help scientists make new 
medical discoveries or even make mundane, time-consuming activities 
more efficient.\5\
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    \3\ See e.g., Elizabeth Seger et al., Open-Sourcing Highly 
Capable Foundation Models, Centre for the Governance of AI (2023) 
https://cdn.governance.ai/Open-Sourcing_Highly_Capable_Foundation_Models_2023_GovAI.pdf.
    \4\ See e.g., Executive Office of the President: Office of 
Management and Budget, Proposed Memorandum For the Heads of 
Executive Departments and Agencies (November 3, 2023) https://www.whitehouse.gov/wp-content/uploads/2023/11/AI-in-Government-Memo-draft-for-public-review.pdf; Cui Beilei et al., Surgical-DINO: 
Adapter Learning of Foundation Model for Depth Estimation in 
Endoscopic Surgery, arXiv:2401.06013v1 (January 11, 2024) https://arxiv.org/pdf/2401.06013.pdf (Using low-ranked adaptation, or LoRA, 
in a foundation model to help with surgical depth estimation for 
endoscopic surgeries).
    \5\ See e.g., Shaoting Zhang, On the Challenges and Perspectives 
of Foundation Models for Medical Image Analysis, arXiv:2306.05705v2 
(November 23, 2023), https://arxiv.org/pdf/2306.05705.pdf.
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    Open foundation models have the potential to transform research, 
both within computer science \6\ and through supporting other 
disciplines such as medicine, pharmaceutical, and scientific 
research.\7\ Historically, widely available programming libraries have 
given researchers the ability to simultaneously run and understand 
algorithms created by other programmers. Researchers and journals have 
supported the movement towards open science,\8\ which includes sharing 
research artifacts like the data and code required to reproduce 
results.
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    \6\ See e.g., David Noever, Can Large Language Models Find And 
Fix Vulnerable Software?, arxiv 2308.10345 (August 20, 2023) https://arxiv.org/abs/2308.10345; \6\ Andreas St[ouml]ckl, Evaluating a 
Synthetic Image Dataset Generated with Stable Diffusion, Proceedings 
of Eighth International Congress on Information and Communication 
Technology Vol. 693 (July 25, 2023) https://link.springer.com/chapter/10.1007/978-981-99-3243-6_64.
    \7\ See e.g., Kun-Hsing Yu et al., Artificial intelligence in 
healthcare, Nature Biomedical Engineering Vol. 2 719-731 (October 
10, 2018) https://www.nature.com/articles/s41551-018-0305-z#citeas; 
Kevin Maik Jablonka et al., 14 examples of how LLMs can transform 
materials science and chemistry: a reflection on a large language 
model hackathon, Digital Discovery 2 (August 8, 2023) https://pubs.rsc.org/en/content/articlehtml/2023/dd/d3dd00113j.
    \8\ See e.g., Harvey V. Fineberg et al., Consensus Study Report: 
Reproducibility and Replicability in Science, National Academies of 
Sciences (May 2019) https://nap.nationalacademies.org/resource/25303/R&R.pdf; Nature, Reporting standards and availability of data, 
materials, code and protocols, https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards; Science, Science 
Journals: Editorial Policies, https://www.science.org/content/page/science-journals-editorial-policies#data-and-code-deposition; Edward 
Miguel, Evidence on Research Transparency in Economics, Journal of 
Economic Perspectives Vol. 35 No. 3 (2021) https://www.aeaweb.org/articles?id=10.1257/jep.35.3.193.
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    Open foundation models can allow for more transparency and enable 
broader access to allow greater oversight by technical experts, 
researchers, academics, and those from the security community.\9\ 
Foundation models with widely available model weights could also 
promote competition in downstream markets for which AI models are a 
critical input, allowing smaller players to add value by adjusting 
models originally produced by the large developers.\10\ The 
accessibility

[[Page 14061]]

of open foundation models also provides tools for individuals and civil 
society groups to resist authoritarian regimes, furthering democratic 
values and U.S. foreign policy goals.
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    \9\ See e.g., Rishi Bommasani et al., Considerations for 
Governing Open Foundation Models, Stanford University Human-Centered 
Artificial Intelligence (December 2023) https://hai.stanford.edu/sites/default/files/2023-12/Governing-Open-Foundation-Models.pdf.
    \10\ See, e.g., Jai Vipra and Anton Korinek, Market 
concentration implications of foundation models: The Invisible Hand 
of ChatGPT, Brookings Inst. (2023) https://www.brookings.edu/articles/market-concentration-implications-of-foundation-models-the-invisible-hand-of-chatgpt/.
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    While open foundation models potentially offer significant 
benefits, they may pose risks as well. Foundation models with widely-
available model weights could engender substantial harms, such as risks 
to security, equity, civil rights, or other harms due to, for 
instance,\11\ affirmative misuse, failures of effective oversight, or 
lack of clear accountability mechanisms.\12\ Others argue that these 
open foundation models enable development of attacks against 
proprietary models due to similarities in the data sets used to train 
them.\13\ The wide availability of dual use foundation models with 
widely available model weights and the continually shrinking amount of 
compute necessary to fine-tune these models together create 
opportunities for malicious actors to use such models to engage in 
harm.\14\ The lack of monitoring of open foundation models may worsen 
existing challenges, for example, by easing creation of synthetic non-
consensual intimate images or enabling mass disinformation 
campaigns.\15\
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    \11\ Id.
    \12\ Id.
    \13\ For example, researchers have found ways to get both black 
box large language models as well as more open models to produce 
objectionable content through adversarial attacks. See e.g., Andy 
Zou et al., Universal and Transferable Adversarial Attacks on 
Aligned Language Models, arXiv:2307.15043 (July 27, 2023). https://arxiv.org/abs/2307.15043 (``Surprisingly, we find that the 
adversarial prompts generated by our approach are quite 
transferable, including to black-box, publicly released LLMs . . . 
When doing so, the resulting attack suffix is able to induce 
objectionable content in the public interfaces to ChatGPT, Bard, and 
Claude, as well as open source LLMs such as LLaMA-2-Chat, Pythia, 
Falcon, and others.'').
    \14\ See e.g., Zoe Brammer, How Does Access Impact Risk? 
Assessing AI Foundation Model Risk Along a Gradient of Access, The 
Institute for Security and Technology (December 2023) https://securityandtechnology.org/wp-content/uploads/2023/12/How-Does-Access-Impact-Risk-Assessing-AI-Foundation-Model-Risk-Along-A-Gradient-of-Access-Dec-2023.pdf.
    \15\ Id and see e.g. Pranshu Verma, The rise of AI fake news is 
creating a `misinformation superspreader', Washington Post (December 
17, 2023) https://www.washingtonpost.com/technology/2023/12/17/ai-fake-news-misinformation/.
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    On October 30, 2023, President Biden signed the Executive order on 
``Safe, Secure, and Trustworthy Development and Use of Artificial 
Intelligence.'' \16\ Noting the importance of maximizing the benefits 
of open foundation models while managing and mitigating the attendant 
risks, section 4.6 the Executive order tasked the Secretary of 
Commerce, acting through NTIA and in consultation with the Secretary of 
State, with soliciting feedback ``from the private sector, academia, 
civil society, and other stakeholders through a public consultation 
process on the potential risks, benefits, other implications, and 
appropriate policy and regulatory approaches related to dual-use 
foundation models for which the model weights are widely available.'' 
\17\ As required by the Executive order, the Secretary of Commerce, 
through NTIA, and in consultation with the Secretary of State, will 
author a report to the President on the ``potential benefits, risks, 
and implications of dual-use foundation models for which the model 
weights are widely available, as well as policy and regulatory 
recommendations pertaining to those models.'' \18\
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    \16\ E.O. 14110, 88 FR 75191 (November 1, 2023).
    \17\ Id.
    \18\ Id.
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    In particular, the Executive order asks NTIA to consider risks and 
benefits of dual-use foundation models with weights that are ``widely 
available.'' \19\ Likewise, ``openness'' or ``wide availability'' of 
model weights are also terms without clear definition or consensus. 
There are gradients of ``openness,'' ranging from fully ``closed'' to 
fully ``open.'' \20\ There is also more information needed to detail 
the relationship between openness and the wide availability of both 
model weights and open foundation models more generally. This could 
include, for example, information about what types of licenses and 
distribution methods are available or could be available for open 
foundation models, and how such licenses and distribution methods fit 
within an understanding of openness and wide availability.\21\
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    \19\ E.O. 14110, 88 FR 75191 (November 1, 2023).
    \20\ See, e.g., Irene Solaiman, The Gradient of Generative AI 
Release: Methods and Considerations, arXiv:2302.04844v1 (February 5, 
2023) https://arxiv.org/pdf/2302.04844.pdf; Bommasani et al., supra 
note 9.
    \21\ See, e.g., Carlos Munoz Ferrandis, OpenRAIL: Towards open 
and responsible AI licensing frameworks, Hugging Face Blog (August 
31, 2022) https://huggingface.co/blog/open_rail; Danish Contractor 
et al., Behavioral Use Licensing for Responsible AI, 
arXiv:2011.03116v2 (October 20, 2022) https://arxiv.org/pdf/2011.03116.pdf.
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    NTIA also requests input on any potential regulatory models, either 
voluntary or mandatory, that could maintain and potentially increase 
the benefits and/or mitigate the risks of dual use foundation models 
with widely available model weights. We seek input as to different 
kinds of regulatory structures that could deal with not only the large 
scale of these foundation models, but also the declining level of 
computing resources needed to fine-tune and retrain them.

Definitions

    This Request for Comment uses the terms defined in sec. 3 of the 
Executive order. In addition, we use broader terms interchangeably for 
both ease of understanding and clarity, as set forth below. 
``Artificial intelligence'' or ``AI'' refer to a machine-based system 
that can, for a given set of human-defined objectives, make 
predictions, recommendations, or decisions, influencing real or virtual 
environments.\22\ Artificial intelligence systems use machine- and 
human-based inputs to perceive real and virtual environments, abstract 
such perceptions into models through analysis in an automated manner, 
and use model inference to formulate options for information or action.
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    \22\ E.O. 14110, 88 FR 75191 (November 1, 2023).
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    Foundation models are typically defined as, ``powerful models that 
can be fine-tuned and used for multiple purposes.'' \23\ Under the 
Executive order, a ``dual-use foundation model'' is ``an AI model that 
is trained on broad data; generally uses self-supervision, contains at 
least tens of billions of parameters; is applicable across a wide range 
of contexts; and that exhibits, or could be easily modified to exhibit, 
high levels of performance at tasks that pose a serious risk to 
security, national economic security, national public health or safety, 
or any combination of those matters . . . .'' \24\ Both definitions of 
``foundation model'' and of ``dual-use foundation model''--highlight 
the key trait of these models, that they can be used in a number of 
ways.\25\
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    \23\ See, e.g., ``A foundation model is any model that is 
trained on broad data (generally using self-supervision at scale) 
that can be adapted (e.g., fine-tuned) to a wide range of downstream 
tasks[.]'' Rishi Bommasani et al., On the Opportunities and Risks of 
Foundation Models, arXiv:2108.07258v3 (July 12, 2022). https://arxiv.org/pdf/2108.07258.pdf.
    \24\ E.O. 14110, 88 FR 75191 (November 1, 2023).
    \25\ Id.
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    ``Generative AI can be understood as a form of AI model 
specifically intended to produce new digital material as an output 
(including text, images, audio, video, software code), including when 
such AI models are used in applications and their user interfaces.'' 
\26\ The term ``generative AI'' refers to a class of AI models built on 
foundation models

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``that emulate the structure and characteristics of input data in order 
to generate derived synthetic content.'' \27\ Chatbots like ChatGPT, 
large language models like BLOOM, and image generators like Midjourney 
are all examples of generative AI.
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    \26\ G7 Hiroshima Process on Generative Artificial Intelligence 
(AI) Towards a G7 Common Understanding on Generative AI, 
Organisation for Economic Co-operation and Development (OECD) 
(September 7, 2023) https://www.oecd-ilibrary.org/docserver/bf3c0c60-en.pdf?expires=1705032283&id=id&accname=guest&checksum=85A1D78C60AC6D8BBFBF2514CB7F2A5D.
    \27\ E.O. 14110, 88 FR 75191 (November 1, 2023).
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    This Request for Comment is particularly focused on the wide 
availability, such as being publicly posted online, of foundation model 
weights. ``Model weights'' are ``numerical parameter[s] within an AI 
model that help [. . .] determine the model's output in response to 
inputs.'' \28\ In addition to model weights, there are other 
``components'' of an AI model, including training data, code, or other 
elements, which are involved in its development or use, and may or may 
not be made widely available.
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    \28\ Id.
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    The Executive order directs NTIA to focus on dual-use foundation 
models that were trained on broad data; generally use self-supervision; 
contain at least tens of billions of parameters; are applicable across 
a wide range of contexts; and exhibit, or could be easily modified to 
exhibit, high levels of performance at tasks that pose a serious risk 
to security, national economic security, national public health or 
safety, or any combination of those matter.\29\ NTIA also remains 
interested in the discussion of models that fall outside of the scope 
of this Request for Comments in order to better understand the current 
landscape and potential impact of regulatory or policy actions.
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    \29\ Id.
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Instructions for Commenters

    Through this Request for Comment, we hope to gather information on 
the following questions. These are not exhaustive, and commenters are 
invited to provide input on relevant questions not asked below. 
Commenters are not required to respond to all questions. When 
responding to one or more of the questions below, please note in the 
text of your response the number of the question to which you are 
responding. Commenters should include a page number on each page of 
their submissions. Commenters are welcome to provide specific 
actionable proposals, rationales, and relevant facts.
    Please do not include in your comments information of a 
confidential nature, such as sensitive personal information or 
proprietary information. All comments received are a part of the public 
record and will generally be posted to Regulations.gov without change. 
All personal identifying information (e.g., name, address) voluntarily 
submitted by the commenter may be publicly accessible.

Questions

    1. How should NTIA define ``open'' or ``widely available'' when 
thinking about foundation models and model weights?
    a. Is there evidence or historical examples suggesting that weights 
of models similar to currently-closed AI systems will, or will not, 
likely become widely available? If so, what are they?
    b. Is it possible to generally estimate the timeframe between the 
deployment of a closed model and the deployment of an open foundation 
model of similar performance on relevant tasks? How do you expect that 
timeframe to change? Based on what variables? How do you expect those 
variables to change in the coming months and years?
    c. Should ``wide availability'' of model weights be defined by 
level of distribution? If so, at what level of distribution (e.g., 
10,000 entities; 1 million entities; open publication; etc.) should 
model weights be presumed to be ``widely available''? If not, how 
should NTIA define ``wide availability?''
    d. Do certain forms of access to an open foundation model (web 
applications, Application Programming Interfaces (API), local hosting, 
edge deployment) provide more or less benefit or more or less risk than 
others? Are these risks dependent on other details of the system or 
application enabling access?
    i. Are there promising prospective forms or modes of access that 
could strike a more favorable benefit-risk balance? If so, what are 
they?
    2. How do the risks associated with making model weights widely 
available compare to the risks associated with non-public model 
weights?
    a. What, if any, are the risks associated with widely available 
model weights? How do these risks change, if at all, when the training 
data or source code associated with fine tuning, pretraining, or 
deploying a model is simultaneously widely available?
    b. Could open foundation models reduce equity in rights and safety-
impacting AI systems (e.g., healthcare, education, criminal justice, 
housing, online platforms, etc.)?
    c. What, if any, risks related to privacy could result from the 
wide availability of model weights?
    d. Are there novel ways that state or non-state actors could use 
widely available model weights to create or exacerbate security risks, 
including but not limited to threats to infrastructure, public health, 
human and civil rights, democracy, defense, and the economy?
    i. How do these risks compare to those associated with closed 
models?
    ii. How do these risks compare to those associated with other types 
of software systems and information resources?
    e. What, if any, risks could result from differences in access to 
widely available models across different jurisdictions?
    f. Which are the most severe, and which the most likely risks 
described in answering the questions above? How do these set of risks 
relate to each other, if at all?
    3. What are the benefits of foundation models with model weights 
that are widely available as compared to fully closed models?
    a. What benefits do open model weights offer for competition and 
innovation, both in the AI marketplace and in other areas of the 
economy? In what ways can open dual-use foundation models enable or 
enhance scientific research, as well as education/training in computer 
science and related fields?
    b. How can making model weights widely available improve the 
safety, security, and trustworthiness of AI and the robustness of 
public preparedness against potential AI risks?
    c. Could open model weights, and in particular the ability to 
retrain models, help advance equity in rights and safety-impacting AI 
systems (e.g., healthcare, education, criminal justice, housing, online 
platforms etc.)?
    d. How can the diffusion of AI models with widely available weights 
support the United States' national security interests? How could it 
interfere with, or further the enjoyment and protection of human rights 
within and outside of the United States?
    e. How do these benefits change, if at all, when the training data 
or the associated source code of the model is simultaneously widely 
available?
    4. Are there other relevant components of open foundation models 
that, if simultaneously widely available, would change the risks or 
benefits presented by widely available model weights? If so, please 
list them and explain their impact.
    5. What are the safety-related or broader technical issues involved 
in managing risks and amplifying benefits of dual-use foundation models 
with widely available model weights?
    a. What model evaluations, if any, can help determine the risks or 
benefits associated with making weights of a foundation model widely 
available?
    b. Are there effective ways to create safeguards around foundation 
models,

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either to ensure that model weights do not become available, or to 
protect system integrity or human well-being (including privacy) and 
reduce security risks in those cases where weights are widely 
available?
    c. What are the prospects for developing effective safeguards in 
the future?
    d. Are there ways to regain control over and/or restrict access to 
and/or limit use of weights of an open foundation model that, either 
inadvertently or purposely, have already become widely available? What 
are the approximate costs of these methods today? How reliable are 
they?
    e. What if any secure storage techniques or practices could be 
considered necessary to prevent unintentional distribution of model 
weights?
    f. Which components of a foundation model need to be available, and 
to whom, in order to analyze, evaluate, certify, or red-team the model? 
To the extent possible, please identify specific evaluations or types 
of evaluations and the component(s) that need to be available for each.
    g. Are there means by which to test or verify model weights? What 
methodology or methodologies exist to audit model weights and/or 
foundation models?
    6. What are the legal or business issues or effects related to open 
foundation models?
    a. In which ways is open-source software policy analogous (or not) 
to the availability of model weights? Are there lessons we can learn 
from the history and ecosystem of open-source software, open data, and 
other ``open'' initiatives for open foundation models, particularly the 
availability of model weights?
    b. How, if at all, does the wide availability of model weights 
change the competition dynamics in the broader economy, specifically 
looking at industries such as but not limited to healthcare, marketing, 
and education?
    c. How, if at all, do intellectual property-related issues--such as 
the license terms under which foundation model weights are made 
publicly available--influence competition, benefits, and risks? Which 
licenses are most prominent in the context of making model weights 
widely available? What are the tradeoffs associated with each of these 
licenses?
    d. Are there concerns about potential barriers to interoperability 
stemming from different incompatible ``open'' licenses, e.g., licenses 
with conflicting requirements, applied to AI components? Would 
standardizing license terms specifically for foundation model weights 
be beneficial? Are there particular examples in existence that could be 
useful?
    7. What are current or potential voluntary, domestic regulatory, 
and international mechanisms to manage the risks and maximize the 
benefits of foundation models with widely available weights? What kind 
of entities should take a leadership role across which features of 
governance?
    a. What security, legal, or other measures can reasonably be 
employed to reliably prevent wide availability of access to a 
foundation model's weights, or limit their end use?
    b. How might the wide availability of open foundation model weights 
facilitate, or else frustrate, government action in AI regulation?
    c. When, if ever, should entities deploying AI disclose to users or 
the general public that they are using open foundation models either 
with or without widely available weights?
    d. What role, if any, should the U.S. government take in setting 
metrics for risk, creating standards for best practices, and/or 
supporting or restricting the availability of foundation model weights?
    i. Should other government or non-government bodies, currently 
existing or not, support the government in this role? Should this vary 
by sector?
    e. What should the role of model hosting services (e.g., 
HuggingFace, GitHub, etc.) be in making dual-use models with open 
weights more or less available? Should hosting services host models 
that do not meet certain safety standards? By whom should those 
standards be prescribed?
    f. Should there be different standards for government as opposed to 
private industry when it comes to sharing model weights of open 
foundation models or contracting with companies who use them?
    g. What should the U.S. prioritize in working with other countries 
on this topic, and which countries are most important to work with?
    h. What insights from other countries or other societal systems are 
most useful to consider?
    i. Are there effective mechanisms or procedures that can be used by 
the government or companies to make decisions regarding an appropriate 
degree of availability of model weights in a dual-use foundation model 
or the dual-use foundation model ecosystem? Are there methods for 
making effective decisions about open AI deployment that balance both 
benefits and risks? This may include responsible capability scaling 
policies, preparedness frameworks, et cetera.
    j. Are there particular individuals/entities who should or should 
not have access to open-weight foundation models? If so, why and under 
what circumstances?
    8. In the face of continually changing technology, and given 
unforeseen risks and benefits, how can governments, companies, and 
individuals make decisions or plans today about open foundation models 
that will be useful in the future?
    a. How should these potentially competing interests of innovation, 
competition, and security be addressed or balanced?
    b. Noting that E.O. 14110 grants the Secretary of Commerce the 
capacity to adapt the threshold, is the amount of computational 
resources required to build a model, such as the cutoff of 10\26\ 
integer or floating-point operations used in the Executive order, a 
useful metric for thresholds to mitigate risk in the long-term, 
particularly for risks associated with wide availability of model 
weights?
    c. Are there more robust risk metrics for foundation models with 
widely available weights that will stand the test of time? Should we 
look at models that fall outside of the dual-use foundation model 
definition?
    9. What other issues, topics, or adjacent technological 
advancements should we consider when analyzing risks and benefits of 
dual-use foundation models with widely available model weights?

    Dated: February 20, 2024.
Stephanie Weiner,
Chief Counsel, National Telecommunications and Information 
Administration.
[FR Doc. 2024-03763 Filed 2-23-24; 8:45 am]
BILLING CODE 3510-60-P