Dual Use Foundation Artificial Intelligence Models With Widely Available Model Weights, 14059-14063 [2024-03763]
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Federal Register / Vol. 89, No. 38 / Monday, February 26, 2024 / Notices
acceptable estimate of the individual
marine mammals taken is available, if
the estimated number of individual
animals taken is up to, but not greater
than, one-third of the best available
abundance estimate, NMFS will
determine that the numbers of marine
mammals taken of a species or stock are
small. For more information please see
NMFS’ discussion of the MMPA’s small
numbers requirement provided in the
final rule (86 FR 5322, 86 FR 5438,
January 19, 2021).
The take numbers for authorization,
which are determined as described
above, are used by NMFS in making the
necessary small numbers
determinations through comparison
with the best available abundance
estimates (see discussion at 86 FR 5322,
86 FR 5391, January 19, 2021). For this
comparison, NMFS’ approach is to use
the maximum theoretical population,
determined through review of current
stock assessment reports (SAR; https://
www.fisheries.noaa.gov/national/
marine-mammal-protection/marinemammal-stock-assessments) and modelpredicted abundance information
(https://seamap.env.duke.edu/models/
Duke/GOM/). For the latter, for taxa
where a density surface model could be
produced, we use the maximum mean
seasonal (i.e., 3-month) abundance
prediction for purposes of comparison
as a precautionary smoothing of monthto-month fluctuations and in
consideration of a corresponding lack of
data in the literature regarding seasonal
distribution of marine mammals in the
GOM. Information supporting the small
numbers determinations is provided in
table 1.
TABLE 1—TAKE ANALYSIS
Authorized
take 1
Species
Rice’s whale .................................................................................................................................
Sperm whale ................................................................................................................................
Kogia spp .....................................................................................................................................
Beaked whales ............................................................................................................................
Rough-toothed dolphin ................................................................................................................
Bottlenose dolphin .......................................................................................................................
Clymene dolphin ..........................................................................................................................
Atlantic spotted dolphin ...............................................................................................................
Pantropical spotted dolphin .........................................................................................................
Spinner dolphin ............................................................................................................................
Striped dolphin .............................................................................................................................
Fraser’s dolphin ...........................................................................................................................
Risso’s dolphin .............................................................................................................................
Melon-headed whale ...................................................................................................................
Pygmy killer whale .......................................................................................................................
False killer whale .........................................................................................................................
Killer whale ..................................................................................................................................
Short-finned pilot whale ...............................................................................................................
0
132
3 50
580
100
473
281
189
1,274
341
110
4 32
83
185
43
69
0
53
Abundance 2
51
2,207
4,373
3,768
4,853
176,108
11,895
74,785
102,361
25,114
5,229
1,665
3,764
7,003
2,126
3,204
267
1,981
Percent
abundance
n/a
6.0
1.1
15.4
2.1
0.3
2.4
0.3
1.2
1.4
2.1
1.9
2.2
2.6
2.0
2.2
n/a
2.7
1 Scalar
ratios were not applied in this case due to brief survey duration.
abundance estimate. For most taxa, the best abundance estimate for purposes of comparison with take estimates is considered here to
be the model-predicted abundance (Roberts et al., 2016). For those taxa where a density surface model predicting abundance by month was
produced, the maximum mean seasonal abundance was used. For those taxa where abundance is not predicted by month, only mean annual
abundance is available. For Rice’s whale and killer whale, the larger estimated SAR abundance estimate is used.
3 Includes 3 take by Level A harassment and 47 takes by Level B harassment.
4 Modeled exposure estimate less than assumed average group size (Maze-Foley and Mullin, 2006).
2 Best
Based on the analysis contained
herein of LLOG’s planned survey
activity described in its LOA
application and the anticipated take of
marine mammals, NMFS finds that
small numbers of marine mammals will
be taken relative to the affected species
or stock sizes (i.e., less than one-third of
the best available abundance estimate)
and therefore the taking is of no more
than small numbers.
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Authorization
NMFS has determined that the level
of taking for this LOA request is
consistent with the findings made for
the total taking allowable under the
incidental take regulations and that the
amount of take authorized under the
LOA is of no more than small numbers.
Accordingly, we have issued an LOA to
LLOG authorizing the take of marine
mammals incidental to its geophysical
survey activity, as described above.
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Dated: February 20, 2024.
Kimberly Damon-Randall,
Director, Office of Protected Resources,
National Marine Fisheries Service.
[FR Doc. 2024–03788 Filed 2–23–24; 8:45 am]
BILLING CODE 3510–22–P
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
National Telecommunications
and Information Administration,
Department of Commerce.
ACTION: Notice; request for comment.
AGENCY:
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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
SUMMARY:
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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
thall@ntia.gov 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: press@ntia.gov.
SUPPLEMENTARY INFORMATION:
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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.
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-trustworthydevelopment-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.
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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
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 finetuned 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 safetyimpacting 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
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://securityand
technology.org/wp-content/uploads/2023/12/HowDoes-Access-Impact-Risk-Assessing-AI-FoundationModel-Risk-Along-A-Gradient-of-Access-Dec2023.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.
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-publicreview.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
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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.
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
Image Analysis, arXiv:2306.05705v2 (November 23,
2023), https://arxiv.org/pdf/2306.05705.pdf.
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 Sto¨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/10.1007/978-981-993243-6_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/s41551-018-0305z#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/editorialpolicies/reporting-standards; Science, Science
Journals: Editorial Policies, https://
www.science.org/content/page/science-journalseditorial-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.
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-FoundationModels.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/marketconcentration-implications-of-foundation-modelsthe-invisible-hand-of-chatgpt/.
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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.
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
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
11 Id.
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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-AssessingAI-Foundation-Model-Risk-Along-A-Gradient-ofAccess-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-newsmisinformation/.
16 E.O. 14110, 88 FR 75191 (November 1, 2023).
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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
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
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
17 Id.
18 Id.
19 E.O.
14110, 88 FR 75191 (November 1, 2023).
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.
20 See,
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computing resources needed to finetune 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.
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
‘‘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
22 E.O.
14110, 88 FR 75191 (November 1, 2023).
e.g., ‘‘A foundation model is any model
that is trained on broad data (generally using selfsupervision at scale) that can be adapted (e.g., finetuned) 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.
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=85A1D78C60AC
6D8BBFBF2514CB7F2A5D.
23 See,
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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.
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.
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.
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
27 E.O.
14110, 88 FR 75191 (November 1, 2023).
28 Id.
29 Id.
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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 safetyimpacting 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,
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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 safetyimpacting 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
VerDate Sep<11>2014
16:23 Feb 23, 2024
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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 nongovernment 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
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14063
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 1026
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]
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[Docket No. 2024–0006; OMB Control No.
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ACTION: Notice and request for
comments regarding a proposed
AGENCY:
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Agencies
[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.
-----------------------------------------------------------------------
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\
---------------------------------------------------------------------------
\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.
---------------------------------------------------------------------------
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\
---------------------------------------------------------------------------
\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.
---------------------------------------------------------------------------
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.
---------------------------------------------------------------------------
\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.
---------------------------------------------------------------------------
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.
---------------------------------------------------------------------------
\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/.
---------------------------------------------------------------------------
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\
---------------------------------------------------------------------------
\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/.
---------------------------------------------------------------------------
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\
---------------------------------------------------------------------------
\16\ E.O. 14110, 88 FR 75191 (November 1, 2023).
\17\ Id.
\18\ Id.
---------------------------------------------------------------------------
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\
---------------------------------------------------------------------------
\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.
---------------------------------------------------------------------------
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.
---------------------------------------------------------------------------
\22\ E.O. 14110, 88 FR 75191 (November 1, 2023).
---------------------------------------------------------------------------
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\
---------------------------------------------------------------------------
\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.
---------------------------------------------------------------------------
``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
[[Page 14062]]
``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.
---------------------------------------------------------------------------
\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).
---------------------------------------------------------------------------
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.
---------------------------------------------------------------------------
\28\ Id.
---------------------------------------------------------------------------
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.
---------------------------------------------------------------------------
\29\ Id.
---------------------------------------------------------------------------
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
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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,
[[Page 14063]]
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]
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