[Federal Register Volume 89, Number 193 (Friday, October 4, 2024)]
[Notices]
[Pages 80886-80887]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2024-22974]


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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).

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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 
<a href="/cdn-cgi/l/email-protection#5e3f372d373c37311e30372d2a70393128"><span class="__cf_email__" data-cfemail="b2d3dbc1dbd0dbddf2dcdbc1c69cd5ddc4">[email protected]</span></a> 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 chem-bio 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/wp-content/uploads/2024/05/USG-Policy-for-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

[[Page 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 
post-deployment 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 chem-bio 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 chem-bio 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 Chem-Bio 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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