Federal Register / Vol. 89, No. 38 / Monday, February 26, 2024 / Notices 14059 

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

Species Authorized 

take 1 Abundance 2 Percent 

abundance 

Rice’s whale ................................................................................................................................. 0 51 n/a 

Sperm whale ................................................................................................................................ 132 2,207 6.0 

Kogia spp ..................................................................................................................................... 3 50 4,373 1.1 

Beaked whales ............................................................................................................................ 580 3,768 15.4 

Rough-toothed dolphin ................................................................................................................ 100 4,853 2.1 

Bottlenose dolphin ....................................................................................................................... 473 176,108 0.3 

Clymene dolphin .......................................................................................................................... 281 11,895 2.4 

Atlantic spotted dolphin ............................................................................................................... 189 74,785 0.3 

Pantropical spotted dolphin ......................................................................................................... 1,274 102,361 1.2 

Spinner dolphin ............................................................................................................................ 341 25,114 1.4 

Striped dolphin ............................................................................................................................. 110 5,229 2.1 

Fraser’s dolphin ........................................................................................................................... 4 32 1,665 1.9 

Risso’s dolphin ............................................................................................................................. 83 3,764 2.2 

Melon-headed whale ................................................................................................................... 185 7,003 2.6 

Pygmy killer whale ....................................................................................................................... 43 2,126 2.0 

False killer whale ......................................................................................................................... 69 3,204 2.2 

Killer whale .................................................................................................................................. 0 267 n/a 

Short-finned pilot whale ............................................................................................................... 53 1,981 2.7 

1Scalar ratios were not applied in this case due to brief survey duration. 

2Best 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). 

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. 

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. 

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 

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 


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14060 Federal Register / Vol. 89, No. 38 / Monday, February 26, 2024 / Notices 

1Artificial 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. 

2See 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. 

3See 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. 4See 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). 

5See 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. 

6See 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/10.1007/978-981-99- 

3243-6_64. 

7See 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. 

8See 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. 

9See 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. 

10See, 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/. 

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: 

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

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

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 


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Federal Register / Vol. 89, No. 38 / Monday, February 26, 2024 / Notices 14061 

11 Id. 

12 Id. 

13For 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.’’). 

14See 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/. 

16E.O. 14110, 88 FR 75191 (November 1, 2023). 

17 Id. 

18 Id. 

19E.O. 14110, 88 FR 75191 (November 1, 2023). 

20See, 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. 

21See, 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. 

22E.O. 14110, 88 FR 75191 (November 1, 2023). 

23See, 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. 

24E.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. 

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 

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 

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 


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27E.O. 14110, 88 FR 75191 (November 1, 2023). 

28 Id. 

29 Id. 

‘‘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 

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, 

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, 


khammond on DSKJM1Z7X2PROD with NOTICES
Federal Register / Vol. 89, No. 38 / Monday, February 26, 2024 / Notices 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 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 

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] 

BILLING CODE 3510–60–P 

DEPARTMENT OF DEFENSE 

Defense Acquisition Regulations 

System 

[Docket No. 2024–0006; OMB Control No. 

0750–0004] 

Information Collection Requirement; 

Defense Federal Acquisition 

Regulation Supplement; Assessing 

Contractor Implementation of 

Cybersecurity Requirements 

AGENCY: Defense Acquisition 

Regulations System; Department of 

Defense (DOD). 

ACTION: Notice and request for 

comments regarding a proposed 


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