Notice2024-22974
Safety Considerations for Chemical and/or Biological AI Models
Primary source
Metadata and text below are from the Federal Register, a public-domain U.S. government work. Always verify the official published version before relying on it for any legal matter.
Published
October 4, 2024
Issuing agencies
Commerce DepartmentNational Institute of Standards and Technology
Abstract
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.
Full Text
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<title>Federal Register, Volume 89 Issue 193 (Friday, October 4, 2024)</title>
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[Federal Register Volume 89, Number 193 (Friday, October 4, 2024)]
[Notices]
[Pages 80886-80887]
From the Federal Register Online via the Government Publishing Office [<a href="http://www.gpo.gov">www.gpo.gov</a>]
[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 <a href="http://www.regulations.gov">www.regulations.gov</a> 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 <a href="http://regulations.gov">regulations.gov</a>, including
instructions for accessing agency documents, submitting comments, and
viewing the docket is available at: <a href="http://www.regulations.gov/faq">www.regulations.gov/faq</a>. If you
require an accommodation or cannot otherwise submit your comments via
<a href="http://regulations.gov">regulations.gov</a>, 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:
<a href="https://www.regulations.gov">https://www.regulations.gov</a> under docket number 240920-0247 and at:
<a href="https://www.nist.gov/aisi">https://www.nist.gov/aisi</a> 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, <a href="https://www.whitehouse.gov/wp-content/uploads/2024/05/USG-Policy-for-Oversight-of-DURC-and-PEPP.pdf">https://www.whitehouse.gov/wp-content/uploads/2024/05/USG-Policy-for-Oversight-of-DURC-and-PEPP.pdf</a>).
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 <a href="https://responsiblebiodesign.ai/">https://responsiblebiodesign.ai/</a>).
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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