80886 Federal Register / Vol. 89, No. 193 / Friday, October 4, 2024 / Notices 

would likely lead to the continuation or 

recurrence of dumping, and that the 

magnitude of the margin of dumping 

likely to prevail if the Order is revoked 

for quartz surface products from China 

are weighted-average margins up to 

326.15 percent. 

Administrative Protective Order 

This notice serves as the only 

reminder to parties subject to an 

administrative protective order (APO) of 

their responsibility concerning the 

disposition of proprietary information 

disclosed under APO in accordance 

with 19 CFR 351.305(a). Timely written 

notification of the destruction of APO 

materials or conversion to judicial 

protective order is hereby requested. 

Failure to comply with the regulations 

and terms of an APO is a violation that 

is subject to sanction. 

Notification to Interested Parties 

We are issuing and publishing the 

final results of this expedited sunset 

review in accordance with sections 

751(c), 752(c), and 777(i)(1) of the Act 

and 19 CFR 351.218. 

Dated: September 27, 2024. 

Ryan Majerus, 

Deputy Assistant Secretary for Policy and 

Negotiations, performing the non-exclusive 

functions and duties of the Assistant 

Secretary for Enforcement and Compliance. 

Appendix 

List of Topics Discussed in the Issues and 

Decision Memorandum 

I. Summary 

II. Background 

III. Scope of the Order 

IV. History of the Order 

V. Legal Framework 

VI. Discussion of the Issues 

1. Likelihood of Continuation or 

Recurrence of Dumping 

2. Magnitude of the Margins of Dumping 

Likely to Prevail 

VII. Final Results of Sunset Review 

VIII. Recommendation 

[FR Doc. 2024–22939 Filed 10–3–24; 8:45 am] 

BILLING CODE 3510–DS–P 

DEPARTMENT OF COMMERCE 

National Institute of Standards and 

Technology 

[Docket No. 240920–0247] 

Safety Considerations for Chemical 

and/or Biological AI Models 

AGENCY: U.S. Artificial Intelligence 

Safety Institute (AISI), National Institute 

of Standards and Technology (NIST), 

U.S. Department of Commerce. 

ACTION: Notice; Request for Information 

(RFI). 

SUMMARY: The U.S. Artificial 

Intelligence Safety Institute (AISI), 

housed within the National Institute of 

Standards and Technology (NIST) at the 

Department of Commerce, is seeking 

information and insights from 

stakeholders on current and future 

practices and methodologies for the 

responsible development and use of 

chemical and biological (chem-bio) AI 

models. Chem-bio AI models are AI 

models that can aid in the analysis, 

prediction, or generation of novel 

chemical or biological sequences, 

structures, or functions. We encourage 

respondents to provide concrete 

examples, best practices, case studies, 

and actionable recommendations where 

possible. Responses may inform AISI’s 

overall approach to biosecurity 

evaluations and mitigations. 

DATES: Comments containing 

information in response to this notice 

must be received on or December 3, 

2024, at 11:59 p.m. Eastern time. 

Submissions received after that date 

may not be considered. 

ADDRESSES: Comments must be 

submitted electronically via the Federal 

e-Rulemaking Portal. 

1. Go to www.regulations.gov and 

enter 240920–0247 in the search field, 

2. Click the ‘‘Comment Now!’’ icon, 

complete the required field, including 

the relevant document number and title 

in the subject field, and 

3. Enter or attach your comments. 

Additional information on the use of 

regulations.gov, including instructions 

for accessing agency documents, 

submitting comments, and viewing the 

docket is available at: 

www.regulations.gov/faq. If you require 

an accommodation or cannot otherwise 

submit your comments via 

regulations.gov, please contact NIST 

using the information in the FOR

FURTHER INFORMATION CONTACT section 

below. 

NIST will not accept comments for 

this notice by postal mail, fax, or email. 

To ensure that NIST does not receive 

duplicate copies, please submit your 

comments only once. Comments 

containing references, studies, research, 

and other empirical data that are not 

widely published should include copies 

of the referenced materials. 

All relevant comments received by 

the deadline will be posted at: https:// 

www.regulations.gov under docket 

number 240920–0247 and at: https://

www.nist.gov/aisi without change or 

redaction, so commenters should not 

include information they do not wish to 

be posted publicly (e.g., personal or 

confidential business information). 

FOR FURTHER INFORMATION CONTACT: For 

questions about this RFI contact 

aisibio@nist.gov or Stephanie Guerra, 

U.S. Department of Commerce, 1401 

Constitution Ave. NW, Washington, DC. 

Direct media inquiries to NIST’s Office 

of Public Affairs at (301) 975–2762. 

Users of telecommunication devices for 

the deaf or a text telephone may call the 

Federal Relay Service toll free at 1–800– 

877–8339. 

Accessible Format: NIST will make 

the RFI available in alternate formats, 

such as Braille or large print, upon 

request by persons with disabilities. 

SUPPLEMENTARY INFORMATION: The rapid 

advancement of the use of AI in the 

chemical and biological sciences has led 

to the development of increasingly 

powerful chemical and biological 

(chem-bio) AI models. By reducing the 

time and resources required for 

experimental testing and validation, 

chem-bio AI models can accelerate 

progress in areas such as drug 

discovery, medical countermeasure 

development, and precision medicine. 

However, as with other AI models, there 

is a need to understand and mitigate 

potential risks associated with misuse of 

chem-bio AI models. Examples of chembio AI models include but are not 

limited to foundation models trained 

using chemical and/or biological data, 

protein design tools, small biomolecule 

design tools, viral vector design tools, 

genome assembly tools, experimental 

simulation tools, and autonomous 

experimental platforms. The dual use 

nature of these tools presents unique 

challenges—while they can significantly 

advance beneficial research and 

development, they could also 

potentially be misused to cause harm, 

such as through the design of more 

virulent or toxic pathogens and toxins 

or biological agents that can evade 

existing biosecurity measures. The 

concept of dual use biological research 

is defined in the 2024 United States 

Government Policy for Oversight of 

Dual Use Research of Concern and 

Pathogens with Enhanced Pandemic 

Potential (USG DURC/PEPP Policy, 

https://www.whitehouse.gov/wpcontent/uploads/2024/05/USG-Policyfor-Oversight-of-DURC-and-PEPP.pdf). 

As chem-bio AI models become more 

capable and accessible, it is important to 

proactively address safety and security 

considerations. The scientific 

community has taken steps to address 

these issues, as demonstrated by a 

recent community statement outlining 

values and guiding principles for the 

responsible development of AI 


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Federal Register / Vol. 89, No. 193 / Friday, October 4, 2024 / Notices 80887 

technologies for protein design. This 

statement articulated several voluntary 

commitments in support of such values 

and principles that were adopted by 

agreement by more than one hundred 

individual signatories (see https://

responsiblebiodesign.ai/). 

The following questions are not 

intended to limit the topics that may be 

addressed. Responses may include any 

topic believed to have implications for 

the responsible development and use of 

chem-bio AI models. Respondents need 

not address all statements in this RFI. 

All relevant responses that comply with 

the requirements listed in the DATES and 

ADDRESSES sections of this RFI and set 

forth below will be considered. 

For your organization, or those you 

assist, represent, or are familiar with, 

please provide information on the topics 

below as specifically as possible. NIST 

has provided this non-exhaustive list of 

topics and accompanying questions to 

guide commenters, and the submission 

of any relevant information germane to 

the responsible development and use of 

chem-bio AI models, but that is not 

included in the list of topics below, is 

also encouraged. 

1. Current and/or Possible Future 

Approaches for Assessing Dual-Use 

Capabilities and Risks of Chem-Bio AI 

Models 

a. What current and possible future 

evaluation methodologies, evaluation 

tools, and benchmarks exist for 

assessing the dual-use capabilities and 

risks of chem-bio AI models? 

b. How might existing AI safety 

evaluation methodologies (e.g., 

benchmarking, automated evaluations, 

and red teaming) be applied to chem-bio 

AI models? How can these approaches 

be adapted to potentially specialized 

architectures of chem-bio AI models? 

What are the strengths and limitations 

of these approaches in this specific 

area? 

c. What new or emerging evaluation 

methodologies could be developed for 

evaluating chem-bio AI models that are 

intended for legitimate purposes but 

may output potentially harmful designs? 

d. To what extent is it possible to 

have generalizable evaluation 

methodologies that apply across 

different types of chem-bio AI models? 

To what extent do evaluations have to 

be tailored to specific types of chem-bio 

AI models? 

e. What are the most significant 

challenges in developing better 

evaluations for chem-bio AI models? 

How might these challenges be 

addressed? 

f. How would you include 

stakeholders or experts in the risk 

assessment process? What feedback 

mechanisms would you employ for 

stakeholders to contribute to the 

assessment and ensure transparency in 

the assessment process? 

2. Current and/or Possible Future 

Approaches To Mitigate Risk of Misuse 

of Chem-Bio AI Models 

a. What are current and possible 

future approaches to mitigating the risk 

of misuse of chem-bio AI models? How 

do these strategies address both 

intentional and unintentional misuse? 

b. What mitigations related to the risk 

of misuse of chem-bio AI models are 

currently used or could be applied 

throughout the AI lifecycle (e.g., 

managing training data, securing model 

weights, setting distribution channels 

such as APIs, applying context window 

and output filters, etc.)? 

c. How might safety mitigation 

approaches for other categories of AI 

models, or for other capabilities and 

risks, be applied to chem-bio AI 

models? What are the strengths and 

limitations of these approaches? 

d. What new or emerging safety 

mitigations are being developed that 

could be used to mitigate the risk of 

misuse of chem-bio AI models? To what 

extent do mitigations have to be tailored 

to specific types of chem-bio AI models? 

e. How might the research community 

approach the development and use of 

public and/or proprietary chem-bio 

datasets that could enhance the 

potential harms of chem-bio AI models 

through fine tuning or other postdeployment adaptations? What types of 

datasets might pose the greatest dual use 

risks? What mechanisms exist to ensure 

the safe and responsible use of these 

kinds of datasets? 

3. Safety and Security Considerations 

When Chem-Bio AI Models Interact 

With One Another or Other AI Models 

a. What areas of research are needed 

to better understand the risks associated 

with the interaction of multiple chembio AI models or a chem-bio AI model 

and other AI model into an end-to-end 

workflow or automated laboratory 

environments for synthesizing chem-bio 

materials independent of human 

intervention? (e.g., research involving a 

large language model’s use of a 

specialized chem-bio AI model or tool, 

research into the use of multiple chembio AI models or tools acting in concert, 

etc.)? 

b. What benefits are associated with 

such interactions among AI models? 

c. What strategies exist to identify, 

assess, and mitigate risks associated 

with such interactions among AI models 

while maintaining the beneficial uses? 

4. Impact of Chem-Bio AI Models on 

Existing Biodefense and Biosecurity 

Measures 

a. How might chem-bio AI models 

strengthen and/or weaken existing 

biodefense and biosecurity measures, 

such as nucleic acid synthesis 

screening? 

b. What work has your organization 

done or is your organization currently 

conducting in this area to strengthen 

these existing measures? How can 

chem-bio AI models be used to 

strengthen these measures? 

c. What future research efforts toward 

enhancing, strengthening, refining, and/ 

or developing new biodefense and 

biosecurity measures seem most 

important in the context of chem-bio AI 

models? 

5. Future Safety and Security of ChemBio AI Models 

a. What are the specific areas where 

further research to enhance the safety 

and security of chem-bio AI models is 

most urgent? 

b. How should academia, industry, 

civil society, and government cooperate 

on the topic of safety and security of 

chem-bio AI models? 

c. What are the primary ways in 

which the chem-bio AI model 

community currently cooperates on 

capabilities evaluation of chem-bio AI 

models and/or mitigation of safety and 

security risks of chem-bio AI models? 

How can these organizational structures 

play a role in ongoing efforts to further 

the responsible development and use of 

chem-bio AI models? 

d. What makes it challenging to 

develop and deploy chem-bio AI models 

safely and what collaborative 

approaches could make it easier? 

e. What opportunities exist for 

national AI safety institutes to advance 

safety and security of chem-bio AI 

models? 

f. What opportunities exist for 

national AI safety institutes to create 

and diffuse best practices and ‘‘norms’’ 

related to AI safety in chemical and 

biological research and discovery? 

Alicia Chambers, 

NIST Executive Secretariat. 

[FR Doc. 2024–22974 Filed 10–3–24; 8:45 am] 

BILLING CODE 3510–13–P 


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