Artificial Intelligence in the Biological Sciences: Uses, Safety, Security, and Oversight

November 22, 2023 R47849

Artificial Intelligence in the Biological Sciences: November 22, 2023
Uses, Safety, Security, and Oversight
Todd Kuiken
Artificial intelligence (AI) is a term generally thought of as computerized systems that work and
Analyst in Science and
react in ways commonly thought to require intelligence. AI technologies, methodologies, and
Technology Policy
applications can be used throughout the biological sciences and biology R&D, including in

engineering biology (e.g., the application of engineering principles and the use of systematic
design tools to reprogram cellular systems for a specific functional output). This has enabled

research and development (R&D) advances across multiple application areas and industries. For
example, AI can be used to analyze genomic data (e.g., DNA sequences) to determine the genetic basis of a particular trait
and potentially uncover genetic markers linked with those traits. It has also been used in combination with biological design
tools to aid in characterizing proteins (e.g., 3-D structure) and for designing new chemical structures that can enable specific
medical applications, including for drug discovery. AI can also be used across the scientific R&D process, including the
design of laboratory experiments, protocols to run certain laboratory equipment, and other “de-skilling” aspects of scientific
research. The convergence of AI and other technologies associated with biology can lower technical and knowledge barriers
and increase the number of actors with certain capabilities. These capabilities have potential for beneficial uses while at the
same time raising certain biosafety and biosecurity concerns. For example, some have argued that using AI for biological
design can be repurposed or misused to potentially produce biological and chemical compounds of concern.
Both AI and engineering biology are multidisciplinary fields that build on advances in multiple scientific disciplines and
technical developments that each have associated benefits and risks. As they converge, those benefits and risks may
compound and create unique uncertainties, challenge governance systems, and, in some instances, raise new biosafety and
biosecurity concerns. The U.S. Intelligence Community’s 2023 Annual Threat Assessment stated that the fields of AI and
biotechnology are “being developed and are proliferating faster than companies and governments can shape norms, protect
privacy, and prevent dangerous outcomes.” AI’s use in biology sits within a broader debate on how best to manage biosafety
and biosecurity, including whether that use may lower technical and knowledge barriers and increase the likelihood of malign
use. The potential for the use of AI in conjunction with biological design and other types of scientific research and
experimentation has prompted recommendations from various groups on how to prevent the misuse of AI applications in
biology and science more broadly. These include keeping a human “in the loop,” controlling access to DNA sequences and
synthesis capabilities, and governance mechanisms that restrict, or monitor, who can access certain AI applications and
biological design tools. Numerous bills introduced in the 118th Congress address the risks, benefits, and strategic
competitiveness of AI more generally, while H.R. 4704, S. 2399, and S. 2346 focus more specifically on AI and biological
threats. Other policy considerations for Congress could include the following:
• Whether AI, and its use in biology and other scientific fields, should be regulated broadly across all use
cases and areas of development, regulated on a case-by-case basis focused on particular application areas or
end-use products, or whether current biosafety and biosecurity oversight frameworks are sufficient.
• Whether a broad risk management approach examining the R&D system as a whole is most appropriate,
including at what stage oversight is warranted (e.g., basic research stage, prototype stage, or prior to release
of a final product) and what entity might conduct such oversight (e.g., a federal agency or a system of self-
governance incentives).
• Whether to provide an agency (or agencies) authority to conduct oversight of various aspects of AI, create a
new agency with authority over AI, or authorize agencies under current law to enforce certain oversight
responsibilities. Authorizing agencies under current law may require additional coordination amongst
federal agencies in order to establish oversight responsibilities. Other types of self-governance incentives
could also be examined.
Oversight and governance of the use of AI in the biological sciences involves potential benefits and risks associated with the
biological design capabilities of AI models, as well as the feasibility of producing AI generated designs that pose possible
biosecurity risks. Regulating AI broadly, or the use of AI in one area of R&D, could impact other areas of R&D in specific or
unintended ways. For example, limiting access to AI models, restricting the types of data an AI model may be trained on, or
limiting the capabilities an AI model is allowed to execute, could each impact a biological design tool’s capability.
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Introduction1
Artificial Intelligence (AI) technologies, methodologies, and applications can be used throughout
the biological sciences and biology R&D. AI can generally be thought of as a multidisciplinary
field that studies and develops machines that work and react in ways commonly thought to
require intelligence, such as the ability to learn, solve problems, and achieve goals under
uncertain and varying conditions, with varying levels of autonomy. AI is not one thing; rather, AI
systems can encompass a range of technologies, methodologies, and applications, such as natural
language processing, robotics, and facial recognition. Common terms used in the field of AI
include machine learning (ML), deep learning (DL), and neural networks (NN). ML is an
umbrella term for training computer systems to solve complex problems normally requiring
human intelligence, such as predictions, classification, and association, using a large amount of
data, computer algorithms, statistical models, and other computing techniques.2 DL is a subset of
ML, using techniques such as multi-layer neural networks to improve the performance and
accuracy of problem-solving, for example, in image recognition. An NN represents a set of
algorithms and techniques that process data and extract information at multiple layers of
computational nodes, based on the idea of how neurons in a nervous system signal to one another.
It can be as simple as a single layer with a few nodes, or complex, containing many layers with
millions of nodes. See Figure 1.
Figure 1. Relationship Between AI, ML, DL, Expert Systems, and Statistics

Source: Sue El en Haupt, David John Gagne, and Wil iam W. Hsieh, et al., “The History and Practice of AI in the
Environmental Sciences,” Bulletin of the American Meteorological Society, vol. 103, no. 5 (2022). © American
Meteorological Society. Used with permission.

1 For a general introduction to AI and policy considerations, see CRS Report R47644, Artificial Intelligence: Overview,
Recent Advances, and Considerations for the 118th Congress, by Laurie A. Harris.
2 Adapted from Erik Brynjolfsson, Tom Mitchell, and Daniel Rock, “What Can Machines Learn, and What Does It
Mean for Occupations and the Economy?,” AEA Papers and Proceedings, vol. 108 (May 1, 2018), pp. 43-47,
https://dspace.mit.edu/bitstream/handle/1721.1/120302/pandp.20181019.pdf.
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Machine learning has been used in the biological sciences, and science in general, for some time.3
AI’s ability to process large amounts of raw, unstructured data (e.g., DNA sequence data) has
reduced the time and cost to conduct certain experiments in biology, enabled others types of
experiments that previously were unattainable,4 and contributed to the broader field of
engineering biology. There is no universally agreed definition of engineering biology, but it is
generally thought of as the application of engineering principles and the use of systematic design
tools to enable the reprogramming of cellular systems at the genetic level for a specific functional
output.5 This approach has increased human ability to make direct changes at the cellular level
and create novel genetic material (e.g., DNA and RNA) to obtain specific functions.
Engineering biology is a multidisciplinary field that leverages a broad set of sciences and
technologies.6 Additionally, engineering biology relies on and builds upon advances in other
fields such as nanotechnology, robotics, and, increasingly, artificial intelligence (see Figure 2).7
These technologies enable engineering biology researchers to read and write DNA. For example,
sequencing technologies “read” DNA, while gene synthesis technologies can take sequence data
and “write” DNA, turning the data into physical material which can then be designed or
engineered for different purposes. Other terms such as synthetic biology, biotechnology, and
biological sciences are used interchangeably with engineering biology in this report when
discussed in relation to AI.
Policy concerns have been raised that the use of AI in the biological sciences could be repurposed
or misused. For example, as part of a recent study, an AI model for drug development was
retrained to design molecules for toxicity instead of designing against them. The study reported
that in less than six hours the AI model generated 40,000 molecules that scored within their
desired threshold of toxicity and bioactivity, which included the nerve agent VX, other known
chemical warfare agents, and new molecules that were predicted to have even higher toxicities.8
This report focuses on the use of AI in the biological sciences, particularly engineering biology
and biological design, as well as some aspects of laboratory automation and design. Biological
design tools can be described as the tools and methods that enable the design and understanding
of biological processes (e.g., DNA sequences/synthesis or the design of novel organisms). Such
capabilities have potential positive impacts but at the same time raise certain biosafety and
biosecurity concerns. AI may also have impacts on other fields of science, but those are beyond
the scope of this report.9 The report aims to inform Congress as it considers potential oversight of
AI, either generally, or for specific scientific application areas.

3 Joe G. Greener, Shaun M. Kandathil, and Lewis Moffat, et al., “A Guide to Machine Learning for Biologists,” Nature
Reviews Molecular Cell Biology, vol. 23 (2022).
4 Abhaya Bhardwaj, Shristi Kishore, and Dhananjay K. Pandey, “Artificial Intelligence in Biological Sciences,” Life,
vol. 12, no. 1430 (2022).
5 CRS Report R47265, Synthetic/Engineering Biology: Issues for Congress, by Todd Kuiken.
6 Kent Redford, Thomas Brooks, and Nicholas Macfarlane, et al., Genetic Frontiers for Conservation: An Assessment
of Synthetic Biology and Biodiversity Conservation, IUCN, Technical Assessment, Gland, Switzerland, 2019. National
Academies of Sciences, Engineering, and Medicine, Biodefense in the Age of Synthetic Biology, Washington, DC,
2018, https://doi.org/10.17226/24890.
7 Arlindo L. Oliveira, “Biotechnology, Big Data and Artificial Intelligence,” Biotechnology Journal, vol. 14, no.
1800613 (2019).
8 Fabio Urbina, Filippa Lentzos, and Cédric Invernizzi, et al., “Dual Use of Artificial-Intelligence-Powered Drug
Discovery,” Nature Machine Intelligence, vol. 4 (2022).
9 For examples on how AI is impacting other areas of science, see “How Scientists Are Using Artificial Intelligence,”
The Economist, 2023, https://www.economist.com/science-and-technology/2023/09/13/how-scientists-are-using-
artificial-intelligence.
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Figure 2. Associated Sciences and Technologies Enabling Engineering Biology

Source: Thomas A. Dixon, Paul S. Freemont, and Richard A. Johnson, et al., "A Global Forum on Synthetic
Biology: The Need for International Engagement," Nature Communications, vol. 13, no. 3516 (2022).
Note: Machine learning is typically considered a subfield of AI, but is a specific tool used in engineering biology.
Convergence of AI and Biological Design
AI and the biological sciences have increasingly converged, each building upon the other’s
capabilities to enable new research and development across multiple areas (see Figure 3).10 The
CEO and cofounder of DeepMind, an AI subsidiary of Google, recently said of biology:
At its most fundamental level, I think biology can be thought of as an information
processing system, albeit an extraordinarily complex and dynamic one. Just as mathematics

10 Abhaya Bhardwaj, Shristi Kishore, and Dhananjay K. Pandey, “Artificial Intelligence in Biological Sciences,” Life,
vol. 12, no. 1430 (2022).
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turned out to be the right description language for physics, biology may turn out to be the
perfect type of regime for the application of AI.11
Both AI and engineering biology are multidisciplinary fields that rely on and build upon advances
in other scientific disciplines and technology fields, such as nanotechnology, robotics, and
increasingly big data (e.g., genetic sequence data). Each of these fields is itself a convergence of
multiple sciences and technologies, so their impacts can combine to create new capabilities and
concerns.12 For example, each technology and scientific field in Figure 2 has individual benefits
and risks. As they converge, those benefits and risks can layer upon one another or create unique
opportunities as well as uncertainties that may challenge governance systems, and, in some
instances, raise new biosafety and biosecurity concerns not present in the individual fields
considered separately.
Figure 3. Example Application Areas Where AI and Biological Sciences Converge

Source: CRS, adapted from Abhaya Bhardwaj, Shristi Kishore, and Dhananjay K. Pandey, “Artificial Intelligence
in Biological Sciences,” Life, vol. 12, no. 1430 (2022).
The U.S. Intelligence Community’s 2023 Annual Threat Assessment stated that the fields of AI
and biotechnology are “being developed and are proliferating faster than companies and
governments can shape norms, protect privacy, and prevent dangerous outcomes.”13 The report
went on to identify genomic sequence data as a particular area of interest, pointing towards efforts
by countries, universities, and private companies that have created, or are creating, centralized

11 Demis Hassabis, quoted in Rob Toews, “The Next Frontier for Large Language Models Is Biology,” Forbes, July 16,
2023, https://www.forbes.com/sites/robtoews/2023/07/16/the-next-frontier-for-large-language-models-is-biology/.
12 John T. O’Brien and Cassidy Nelson, “Assessing the Risks Posed by the Convergence of Artificial Intelligence and
Biotechnology,” Health Security, vol. 18, no. 3 (2020).
13 Office of the Director of National Intelligence, Annual Threat Assessment of the U.S. Intelligence Community, 2023,
https://www.dni.gov/files/ODNI/documents/assessments/ATA-2023-Unclassified-Report.pdf.
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databases to collect, store, process, and analyze genetic data. The report further identified China’s
efforts to collect U.S. health and genomic data through its acquisitions of and investments in U.S.
companies, as well as through cyberattacks. This analysis followed a 2021 assessment by the
National Counterintelligence and Security Center suggesting that China understands that the
collection and analysis of large genomic data sets from diverse populations can help foster new
medical discoveries and cures with substantial commercial value, and can advance its AI and
precision medicine industries.14
Importance of AI Related to Genetic Sequence Data and Biological
Design
Some experts have suggested that AI is essential to analyzing the exponential growth of genetic
sequence data.15 According to the National Security Commission on Artificial Intelligence, “AI
will be essential to fully understanding how genetic code interacts with biological processes.”16
AI has the ability to process large amounts of biological data (e.g., genetic sequence data), which
can come from different biological sources. That capability is needed to understand complex
biological systems17 and has become a necessity for certain biological research, such as
engineering biology.18 For example, researchers can use AI to analyze genomic data sets to
determine the genetic basis of a particular trait and potentially uncover genetic markers linked
with that trait.19 Different types of biological data can be utilized by AI and biological design
tools: sequence data, molecular structure data, image data, time-series, and omics data.20 See
Figure 4. A limiting factor, however, is the quality and quantity of the biological data (e.g., DNA
sequences) that the AI system is trained on.21 For example, accurate identification of a particular
species based on DNA requires reference sequences of sufficient quality to exist and be available.
Individual databases have varying standards that govern access, as well as the type and quality of
information they contain. The design, management, quality standards, and data protocols for

14 National Counterintelligence and Security Center, China’s Collection of Genomic and Other Healthcare Data from
America: Risks to Privacy and U.S. Economic and National Security, 2021,
https://www.dni.gov/files/NCSC/documents/SafeguardingOurFuture/NCSC_China_Genomics_Fact_Sheet_2021revisio
n20210203.pdf.
15 Zulema Udaondo, “Big Data and Computational Advancements for Next Generation of Microbial Biotechnology,”
Microbial Biotechnology, vol. 15, no. 1 (2022); Wardah S. Alharbi and Mamoon Rashid, “A Review of Deep Learning
Applications in Human Genomics Using Next-Generation Sequencing Data,” Human Genomics, vol. 16, no. 26 (2022).
16 National Security Commission on Artificial Intelligence, Final Report, 2021, https://www.nscai.gov/2021-final-
report/.
17 Abhaya Bhardwaj, Shristi Kishore, and Dhananjay K. Pandey, “Artificial Intelligence in Biological Sciences,” Life,
vol. 12, no. 1430 (2022).
18 Arlindo L. Oliveira, “Biotechnology, Big Data and Artificial Intelligence,” Biotechnology Journal, vol. 14 (2019).
19 Abhaya Bhardwaj, Shristi Kishore, and Dhananjay K. Pandey, “Artificial Intelligence in Biological Sciences,” Life,
vol. 12, no. 1430 (2022). A trait is a characteristic of an organism that is expressed by genes.
20 William A.V. Beardall, Guy-Bart Stan, and Mary J. Dunlop, “Deep Learning Concepts and Applications for
Synthetic Biology,” GEN Biotechnology, vol. 1, no. 4 (2022). Omics data refers to various disciplines in biology whose
names end in the suffix -omics, such as genomics, proteomics, metabolomics, etc.
21 Fausto Artico, Arthur L Edge III, and Kyle Langham, “The Future of Artificial Intelligence for the BioTech Big Data
Landscape,” Current Opinion in Biotechnology, vol. 76 (2022). Training a model refers to providing a model with data
to learn from, often called a training dataset. After a model is trained to recognize patterns from one dataset, some
models can be provided with new data and still recognize patterns or predict results. Fine-tuning a model refers to
training a previously trained model on new data, or otherwise adjusting an existing model. See CRS Report R47569,
Generative Artificial Intelligence and Data Privacy: A Primer, by Kristen E. Busch.
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reference databases can therefore affect the utility of a particular DNA sequence and its use with
AI.22
Figure 4. Examples of AI Used in Engineering Biology

Source: Wil iam A.V. Beardal , Guy-Bart Stan, and Mary J. Dunlop, “Deep Learning Concepts and Applications
for Synthetic Biology,” GEN Biotechnology, vol. 1, no. 4 (2022).
Notes: “Parts” is a term used in engineering biology to describe a segment, or sequence, of DNA that encodes
for a specific biology function.
Sequencing technologies have evolved rapidly, making it possible to sequence entire genomes
more efficiently and at lower cost.23 Gene synthesis technologies can take sequence data and
“write” DNA, turning it into physical material which can then be designed or engineered for
different purposes. The technologies enabling the ability to both read and write DNA is
fundamental to engineering biology.24
DNA sequences, including human DNA sequences,25 are stored in databases, many of which are
publicly funded and freely accessible, while others are privately held. The volume of genetic
sequence data has grown exponentially as sequencing technology has evolved (see Figure 5 and

22 CRS In Focus IF12285, eDNA/eRNA: Scientific Value in What’s Left Behind, coordinated by Todd Kuiken.
23 For additional analysis on genetic sequence data see CRS In Focus IF12356, Digital Biology: Implications of Genetic
Sequencing, by Todd Kuiken, and CRS In Focus IF12285, eDNA/eRNA: Scientific Value in What’s Left Behind,
coordinated by Todd Kuiken.
24 DNA is made up of two linked strands that form a double helix. These strands are connected by base pairs consisting
of the following bases: Adenine (A), Thymone (T), Cytosine (C), and Guanine (G). A connects with T and C connects
with G.
25 Genetic information is considered health information and protected under the Health Insurance Portability and
Accountability Act (HIPAA). For additional information on genetic research, considerations with the use and misuse of
genetic information, and relevant laws see CRS Report RL34584, The Genetic Information Nondiscrimination Act of
2008 (GINA), by Amanda K. Sarata.
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Figure 6). One analysis identified more than 1,700 databases incorporating data on genomics,
protein sequences, protein structures, plants, and metabolic pathways, among others.26 For
example, the Protein Data Bank, a U.S.-funded data center, contains more than a terabyte of
three-dimensional structure data for biological molecules, including proteins, DNA, and RNA.27
While the Protein Data Bank is an open-source public database, other databases can be
proprietary. For example, Gingko Bioworks claims to have more than 2 billion protein sequences
in its proprietary database.28 Public research groups can also produce large amounts of genetic
sequence data. The Broad Institute claims to produce roughly 500 terabases (trillion bases) of
genomic data per month.29 There is great potential value in the aggregate volume of genetic
datasets that can be collectively mined to discover and characterize relationships among genes.
Figure 5. Cost of DNA Sequencing over Time

Source: CRS analysis of data from Kris A. Wetterstrand, DNA Sequencing Costs: Data, National Institutes of
Health, National Human Genome Research Institute, 2023, https://www.genome.gov/about-genomics/fact-
sheets/DNA-Sequencing-Costs-Data.
Notes: The Y-axis is in logarithmic scale. A megabase (Mb) is a unit of measurement for DNA. One megabase =
1 mil ion bases. The cost per genome assumes a genome size of 3,000 Mb.

26 Arlindo L. Oliveira, “Biotechnology, Big Data and Artificial Intelligence,” Biotechnology Journal, vol. 14 (2019).
27 RCSB Protein Data Bank, About RCSB PDB: Enabling Breakthroughs in Scientific and Biomedical Research and
Education, https://www.rcsb.org/.
28 Ginkgo Bioworks, “Google and Ginkgo: Foundry-Scale Data Meets AI,” press release, 2023,
https://www.ginkgobioworks.com/2023/08/29/google-and-ginkgo-foundry-scale-data-meets-ai/.
29 Broad Institute, Genomics, 2023, https://www.broadinstitute.org/genomics.
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Figure 6. Growth of Sequences in the International Nucleotide Sequence Database
Collaboration

Source: CRS analysis of data from the International Nucleotide Sequence Database Col aboration (INSDC).
Notes: The Y-axis is in logarithmic scale. INSDC includes sequence data from the DNA Data Bank of Japan, the
European Nucleotide Archive, and GenBank, the National Institutes of Health genetic sequence database.
The U.S. National Security Commission on Artificial Intelligence recommended that the U.S.
fund and prioritize the development of a biobank containing a “wide range of high-quality
biological and genetic data sets securely accessible by researchers.” 30 The Commission further
noted that the establishment of such a database containing a broad range of human, animal, and
plant genomes would enhance and democratize biotechnology innovations and facilitate new
levels of AI-enabled analysis of genetic data while also reducing U.S. researchers’ reliance on
foreign access to genomic databases for research.31
The availability of genetic data to train AI models, as well as decisions about the selection of
genetic data for that purpose, can introduce bias. For example, training an AI model on datasets
that emphasize or omit certain genetic traits can affect how that information is used and the types
of applications developed, potentially privileging or disadvantaging certain populations. In
addition, access to data and to AI models themselves may impact communities of differing
socioeconomic status or other factors unequally.

30 National Security Commission on Artificial Intelligence, Final Report, 2021, https://www.nscai.gov/2021-final-
report/.
31 Ibid. For additional information on federal initiatives related to DNA databases see CRS In Focus IF12285,
eDNA/eRNA: Scientific Value in What’s Left Behind, coordinated by Todd Kuiken.
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AI-Enabled Biological Design Tools
Proteins are responsible for nearly every task of cellular life. Each has a unique amino acid
sequence, which are the building blocks of proteins, and a complex three-dimensional physical
structure.32 Predicting the three-dimensional structure of proteins has been the focus of research
for more than 50 years, with one of the first research papers on the subject published in 1973.33
According to one paper, the structures of around 100,000 proteins have been deciphered;
however, this is a fraction of the estimated billions of known protein sequences.34 Despite the
increase in genomic sequence data, scientists’ ability to determine protein structures and
reproduce them experimentally has remained a challenge. Understanding protein structures and
how to design new proteins allows researchers to control a protein’s stability and biochemical
properties that can enable specific medical applications, including drug discovery.35 The ability to
predict protein structures can also impact other areas of biochemical engineering, including the
identification of key protein residues and better estimates of optimal kinetic parameters, which
are important for drug development.36
In 2021, an AI-based program called AlphaFold demonstrated the ability to predict protein
structures even when no similar structures are known. The AlphaFold researchers also
demonstrated that its predicted structures were accurate when compared to the experimental
protein structure (i.e., physical material).37 The AI system underpinning AlphaFold incorporated
both physical and biological knowledge about protein structures and was trained on the Protein
Data Bank, a U.S. federally funded open-source data center.38 Another study in 2022 suggested
that AI techniques, specifically deep network hallucination,39 can be used to design new protein
structures that are different from the sequences and structures of naturally occurring proteins.40
Research into designing new proteins typically involves protein structures being predicted based
on previous protein structures. These predictions then have to be experimentally verified for
accuracy (i.e., predicted proteins have to be synthesized and tested, typically in the bacterium E.
coli).41 Most such predictions in the past have not been accurate, and the protein in question could

32 Clare O’Connor and Jill U. Adams, “Proteins Are Responsible for a Diverse Range of Structural and Catalytic
Functions in Cells,” in Essentials of Cell Biology (Cambridge, MA: NPG Education, 2010).
33 Christian B. Anfinsen, “Principles That Govern the Folding of Protein Chains,” Science, vol. 181, no. 4096 (1973).
34 John Jumper, Richard Evans, and Alexander Pritzel, et al., “Highly Accurate Protein Structure Prediction with
AlphaFold,” Nature, vol. 596 (2021).
35 Damiano Sgarbossa, Umberto Lupo, and Anne-Florence, “Generative Power of a Protein Language Model Trained
on Multiple Sequence Alignments,” eLIfe, vol. 12, no. e79854 (2023).
36 Zulema Udaondo, “Big Data and Computational Advancements for Next Generation of Microbial Biotechnology,”
Microbial Biotechnology, vol. 15, no. 1 (2022). Note: Protein residues are individual amino acids that are linked by
peptide bonds in the protein chain. Understanding kinetic parameters can be used for the characterization of disease
processes and for monitoring treatment effects.
37 John Jumper, Richard Evans, and Alexander Pritzel, et al., “Highly Accurate Protein Structure Prediction with
AlphaFold,” Nature, vol. 596 (2021).
38 RCSB Protein Data Bank, About RCSB PDB: Enabling Breakthroughs in Scientific and Biomedical Research and
Education, https://www.rcsb.org/.
39 Hallucination is a term sometimes used to describe the case when an AI model perceives patterns or objects that are
nonexistent or imperceptible to human observers. See IBM, “What Are AI Hallucinations?,”
https://www.ibm.com/topics/ai-hallucinations.
40 B. I. M. Wicky et al., “Hallucinating Symmetric Protein Assemblies,” Science, vol. 378 (2022).
41 E. coli is a bacterium that is used in laboratory experiments to test foreign DNA and their protein products.
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not function within a living organism.42 Recent studies suggest that AI-generated protein
structures may be more accurate, which could accelerate protein engineering by reducing the
number of inaccurately predicted proteins that need to be tested.
While these and other advances show promise, some argue that determining how to efficiently
research and design protein structures with specific functions remains a challenge.43 Novel AI-
generated molecules will still need to be physically synthesized and evaluated to determine
whether they are of any practical use or value beyond the theoretical possibility that was
predicted by the AI model.44
AI-Enabled Laboratory Capabilities
AI-based computer models trained on biological data sets and laboratory experimental procedures
could be used to design experiments to optimize specific conditions (e.g., an organism’s growth
rate). In many engineering and science applications, experiments are conducted and empirical
models are developed with the objective of improving specific responses of interest.45 These
optimization strategies could be combined with generative AI systems,46 including AI large
language models (LLMs) to write computer code instructing laboratory robots to fulfill
experimental parameters and run experiments.47 LLMs are a subset of generative AI and are
characterized as “large” due, in part, to the vast amount of data necessary for training the model
to learn the rules of language.48 This approach could eventually include the use of individual
desk-top DNA synthesizers to synthesize DNA under the direction of the AI output.49 For
example, one study used LLMs, including GPT-4,50 to generate robotic scripts from written
instructions, which were able to program laboratory robots to complete experiments. The study
proposed that “if LLMs can simplify the translation of experimental processes and instructions
into precise robotic movements, then the cost of educating life science researchers to use robots

42 John Jumper, Richard Evans, and Alexander Pritzel, et al., “Highly Accurate Protein Structure Prediction with
AlphaFold,” Nature, vol. 596 (2021).
43 John Ingraham, Max Baranov, and Zak Costello, et al., Illuminating Protein Space with a Programmable Generative
Model, Generate Biomedicines, 2022.
44 W. Patrick Walters and Mark Murcko, “Assessing the Impact of Generative AI on Medicinal Chemistry,” Nature
Biotechnology, vol. 38 (2020).
45 National Institute of Standards and Technology, How do you optimize a process,
https://www.itl.nist.gov/div898/handbook/pri/section5/pri53.htm.
46 Note: Generative AI systems can generate new content—such as text, images, and videos—through learning patterns
from data. See CRS Report R47569, Generative Artificial Intelligence and Data Privacy: A Primer, by Kristen E.
Busch.
47 Abhaya Bhardwaj, Shristi Kishore, and Dhananjay K. Pandey, “Artificial Intelligence in Biological Sciences,” Life,
vol. 12, no. 1430 (2022); Adam C. Dama, Kevin S. Kim, and Danielle M. Leyva, et al., “BacterAI Maps Microbial
Metabolism Without Prior Knowledge,” Nature Microbiology, vol. 8 (2023); Takashi Inagaki, Akari Kato, and Koichi
Takahashi, et al., “LLMs Can Generate Robotic Scripts from Goal-Oriented Instructions in Biological Laboratory
Automation,” arxiv (pre-print), 2023, https://arxiv.org/abs/2304.10267; Daniil A. Boiko, Robert MacKnight, and Gabe
Gomes, “Emergent Autonomous Scientific Research Capabilities of Large Language Models,” arxiv (preprint), 2023,
see https://arxiv.org/abs/2304.05332.
48 CRS Report R47569, Generative Artificial Intelligence and Data Privacy: A Primer, by Kristen E. Busch.
49 Sarah R. Carter, Jaime M. Yassif, and Christopher R. Isaac, Benchtop DNA Synthesis Devices: Capabilities,
Biosecurity Implications, and Governance, Nuclear Threat Initiative, 2023,
https://www.nti.org/analysis/articles/benchtop-dna-synthesis-devices-capabilities-biosecurity-implications-and-
governance/.
50 GPT-4 is an AI chatbot developed by a company called OpenAI underpinned by an LLM.
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can be substantially reduced.”51 The preliminary results suggest that LLMs can interpret and
execute tasks at a level similar to a graduate student working under the guidance of a professor.
However, generating scripts based on longer instructions remains a challenge. Another study
attempted to develop a system, using LLMs, that could be used for autonomous design, planning,
and execution of scientific experiments and concluded that the system “demonstrates exceptional
reasoning and experimental design capabilities, effectively addressing complex problems and
generating high-quality code.”52
One commentary stated that utilizing AI to help standardize certain processes through automation
may help enhance the reproducibility and predictability of biological experiments.53 Some have
argued that AI may reduce the opportunity for human creativity in science, while others suggest
that AI instead will free scientists from some of the repetitive tasks of scientific research and
allow more time for critical thinking.54
Policy Considerations
AI’s use in biology and the broader scientific enterprise could raise policy concerns for Congress,
particularly regarding whether additional oversight may be warranted. There have been numerous
bills introduced in the 118th Congress that address the risks, benefits, and strategic
competitiveness of AI more generally.55 In addition, in July 2023, two bills were introduced that
focused more specifically on AI and biological threats.
• H.R. 4704 and S. 2399, “Artificial Intelligence and Biosecurity Risk Assessment
Act,” would require the Department of Health and Human Services (HHS)
Assistant Secretary for Preparedness and Response to “conduct risk assessments
and implement strategic initiatives or activities to address whether technical
advancements in artificial intelligence, such as open-source artificial intelligence
models and large language models, can be used intentionally or unintentionally to
develop novel pathogens, viruses, bioweapons, or chemical weapons.”
• S. 2346, “Strategy for Public Health Preparedness and Response to Artificial
Intelligence Threats,” would require the Secretary of HHS to develop a strategy
for public health preparedness and response to artificial intelligence threats, and
for other purposes.
Potential Biosafety and Biosecurity Concerns
Some have argued that using GenAI for biological design can be repurposed or misused, raising
certain biosafety and biosecurity concerns.56 As part of a “thought experiment” that may have

51 Takashi Inagaki, Akari Kato, and Koichi Takahashi, et al., “LLMs Can Generate Robotic Scripts from Goal-Oriented
Instructions in Biological Laboratory Automation,” arxiv (pre-print), 2023, https://arxiv.org/abs/2304.10267.
52 Daniil A. Boiko, Robert MacKnight, and Gabe Gomes, “Emergent Autonomous Scientific Research Capabilities of
Large Language Models,” arxiv (preprint), 2023, see https://arxiv.org/abs/2304.05332.
53 Serina L. Robinson, “Artificial Intelligence for Microbial Biotechnology; Beyond the Hype,” Microbial
Biotechnology, vol. 15, no. 1 (2021).
54 Ibid.
55 CRS Report R47644, Artificial Intelligence: Overview, Recent Advances, and Considerations for the 118th
Congress, by Laurie A. Harris.
56 Sean Ekins, Maximilian Brackmann, and Cedric Invernizzi, et al., “Generative Artificial Intelligence-Assisted
Protein Design Must Consider Repurposing Potential,” GEN Biotechnology, vol. 2, no. 4 (2023).
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implications for the Chemical and Biological Weapons Conventions,57 Collaborations
Pharmaceuticals, Inc., retrained its AI model for drug development58 to design molecules for
toxicity instead of designing against them.59 The AI model normally penalizes predicted toxicity
and rewards predicted target activity. By inverting this logic, the AI model was guided to reward
both toxicity and bioactivity. The AI model was then trained with a public database of drug-like
molecules and their bioactivities. The study reported that in less than six hours the AI model
generated 40,000 molecules that scored within their desired threshold of toxicity and bioactivity,
which included the nerve agent VX, other known chemical warfare agents, and new molecules
that were predicted to have even higher toxicities. These new molecules were not included in the
datasets the AI model was trained on. While the AI model was able to generate designs for these
new molecules, the company did not attempt to actually synthesize and produce them.
AI’s potential ability to produce molecules that have similar properties but different chemical
structures or DNA sequences have raised concerns. Specifically, that current detection methods,
and screening guidelines that use certain DNA sequences or other features as identifiers, may not
be able to identify these new toxins of concern. For example, ricin is a plant protein toxin that is
listed on the U.S. Federal Select Agent List.60 Researchers claim that using AI, they could “take
the active site of ricin, computationally remove the surrounding stabilizing protein structure, and
ask a generative algorithm to hallucinate and imprint a new proteinaceous structure around the
active site.”61 This would, in theory, remove the DNA sequence component that is screened by
DNA synthesis service providers62 and other control mechanisms (e.g., the Chemical and
Biological Weapons Conventions or the U.S. Federal Select Agent Program). Note, however, that
using AI to identify and design novel molecules, or DNA sequences, does not mean that the
molecule could actually be produced, or a functioning living organism based on AI-generated
molecules or DNA sequences. Additional scientific, technical, and other challenges would have to
be overcome, such as synthesizing the DNA and getting it to function in a living organism as
designed, to realize this biosecurity risk.63
Laboratory Automation and De-Skilling Concerns
While laboratory automation has already impacted certain research operations (e.g., liquid
handling robots), the use of AI to automate certain procedures that used to require hands-on, tacit
knowledge could expand the pool of individuals who can participate in the biological sciences.
While this could be a positive development in some respects, it could also potentially increase
biosafety and biosecurity concerns.64 One study examined whether such a system would provide
instructions and capabilities to design certain illicit drug compounds and chemical weapons

57 Spiez Convergence Conference, 2021, https://www.spiezlab.admin.ch/en/home/meta/refconvergence.html.
58 Fabio Urbina, Christopher Lowden, and Christopher Culberson, et al., “MegaSyn: Integrating Generative Molecule
Design, Automated Analog Designer and Synthetic Viability Prediction,” ChemRxiv (Preprint), 2021.
59 Fabio Urbina, Filippa Lentzos, and Cédric Invernizzi, et al., “Dual Use of Artificial-Intelligence-Powered Drug
Discovery,” Nature Machine Intelligence, vol. 4 (2022).
60 The Department of Health and Human Services (HHS) and U.S. Department of Agriculture (USDA) Select Agent
Program regulates the possession, use, and transfer of select agents and toxins deemed by HHS or USDA to pose a
severe threat to public health and safety, based on a set of criteria. See http://www.selectagents.gov/.
61 Sean Ekins, Maximilian Brackmann, and Cedric Invernizzi, et al., “Generative Artificial Intelligence-Assisted
Protein Design Must Consider Repurposing Potential,” GEN Biotechnology, vol. 2, no. 4 (2023).
62 International Gene Synthesis Consortium, International Gene Synthesis Consortium Updates Screening Protocols for
Synthetic DNA Products and Services, https://genesynthesisconsortium.org/.
63 John T. O’Brien and Cassidy Nelson, “Assessing the Risks Posed by the Convergence of Artificial Intelligence and
Biotechnology,” Health Security, vol. 18, no. 3 (2020).
64 Ibid.
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agents. While the model refused to return results for certain illicit compounds, the authors suggest
this could be overcome by altering the terminology, prompting the authors to call for restrictions,
particularly a mechanism that would prevent the model from moving forward without the
approval of a “human” who can review and approve certain experiments.65
Wider access to AI, cloud labs,66 genetic sequence information, and gene synthesis capabilities,
including desktop DNA synthesizers,67 have raised other biosafety and biosecurity concerns—
such as who should be able to access these capabilities and what limits might be placed on
synthesis capabilities. The convergence of AI and other technologies associated with biology can
lower technical and knowledge barriers and increase the number of actors with certain
capabilities.68 AI can enable experimentation and design of biological systems, particularly the
design and synthesis of DNA sequences, without the user necessarily understanding what those
biological functions represent or do.
A recent study reported that, in a course at the Massachusetts Institute of Technology (MIT),
students were asked to investigate whether AI chatbots could provide information that could
potentially be used to cause a pandemic. Students were able to use chatbots that suggested
potential pandemic pathogens, explained how they can be generated from synthetic DNA using
reverse genetics, supplied the names of DNA synthesis companies that are unlikely to screen
orders, identified detailed protocols and how to troubleshoot them, and recommended that anyone
lacking the skills to perform reverse genetics engage a core facility or contract research
organization.69 The authors of the study argued that increased access to AI can exacerbate the “de-
skilling” of biological research and that certain restrictions and oversight may be necessary when
considered alongside other advances, such as desktop synthesizers and cloud-based laboratories.
Such restrictions include evaluations of, and controlled access to, LLMs, as well as screenings to
evaluate what a particular person may be attempting to develop, order, or use. Others may counter
that access to information does not necessarily imply the scientific and technical ability to convert
that information into actual harm, in this case the creation of a virus capable of causing a
pandemic.
Automation and “de-skilling” could present increased biosafety and biosecurity concerns. How
severe, or whether these concerns are true in actual practice, is an open debate70 and an area that
Congress may wish to examine. For example, if the output of an LLM poses certain biosafety and
biosecurity risks, should there be restrictions on access to the data (e.g., certain DNA sequences

65 Daniil A. Boiko, Robert MacKnight, and Gabe Gomes, “Emergent Autonomous Scientific Research Capabilities of
Large Language Models,” arxiv (preprint), 2023, see https://arxiv.org/abs/2304.05332.
66 Cloud labs are highly automated laboratories that allow researchers to design and run experiments remotely by
sending “instructions” to a contracted facility via the web. Experiments can include sample preparation, bioassays,
workflows for DNA synthesis, and methods for imaging and detection, as well as AI-driven drug discovery and testing.
“What Are Cloud Labs?,” The Biologist, https://www.rsb.org.uk/biologist-features/the-biologist-s-guide-to-cloud-labs.
67 Sarah R. Carter, Jaime M. Yassif, and Christopher R. Isaac, Benchtop DNA Synthesis Devices: Capabilities,
Biosecurity Implications, and Governance, Nuclear Threat Initiative, 2023,
https://www.nti.org/analysis/articles/benchtop-dna-synthesis-devices-capabilities-biosecurity-implications-and-
governance/.
68 John T. O’Brien and Cassidy Nelson, “Assessing the Risks Posed by the Convergence of Artificial Intelligence and
Biotechnology,” Health Security, vol. 18, no. 3 (2020).
69 Emily H. Soice, Rafael Rocha, and Kimberlee Cordova, et al., “Can Large Language Models Democratize Access to
Dual-Use Biotechnology?,” arxiv, 2023, https://arxiv.org/abs/2306.03809.
70 Sarah R. Carter, Nicole E. Wheeler , and Sabrina Chwalek, et al., The Convergence of Artificial Intelligence and the
Life Sciences: Safeguarding Technology, Rethinking Governance, and Preventing Catastrophe, Nuclear Threat
Initiative , 2023, https://www.nti.org/analysis/articles/the-convergence-of-artificial-intelligence-and-the-life-sciences/;
Helena, Biosecurity in the Age of AI, 2023, https://www.helenabiosecurity.org/.
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of concern) on which LLMs are trained? Restricting access to certain DNA sequences may be
difficult since there are a number of databases currently available globally, a large body of
existing published scientific literature containing sequence data along with research results (a
commonly accepted practice in the global research community), and a number of LLMs that have
already been trained on data from existing databases and literature. There may also be concern
that restricting data that LLMs are trained on could imperil potentially beneficial uses or create
incentives for malicious actors to move certain LLMs to the dark web.
Another option could be restricting access to the LLMs themselves. Developers of the LLMs
could require certain registration, authentication or screening requirements for users to access the
LLM, or particular features of the LLM, that pose potential biosafety and biosecurity concerns.
Additional reporting requirements could also be implemented that identified certain queries or
outputs of the LLM that raised concerns, which potentially could be referred to a federal
oversight or security agency.
Another concern may be the purposes of LLM outputs and what they are being used in
conjunction with. For example, if an LLM is aiding in biological design then the output, or the
mechanisms to produce the output, may be an area where certain restrictions could be
implemented, if found necessary. As discussed below, certain aspects of this concern are
addressed via DNA synthesis screening. However, if certain biological design tools become
ubiquitous (e.g., desk-top DNA synthesizers), Congress may examine whether access to these
tools should be restricted or whether certain aspects of these tools could be constrained. For
example, could the data or biological design features of a desk-top synthesizer be restricted71 to
prevent certain capabilities of producing a biosafety or biosecurity hazard?
Biosafety and Biosecurity Oversight
AI’s use in biology and the broader scientific enterprise sits within a debate on how best to
manage laboratory biosafety and biosecurity.72 Many of the concerns that have been raised around
the convergence of AI and biology stem from information hazards. Information hazards are risks
that arise from the dissemination or the potential dissemination of true information that may cause
harm or enable some agent to cause harm.73 Both AI and certain aspects of biological research can
contain information hazards, either separately or collectively. Addressing these issues has led
some experts to call for controlled access to certain materials, technologies, and equipment, as
well as to develop international norms.74 Some oversight mechanisms are already in place
through U.S. laws and other guidance related to federally funded research,75 as well as industry
best practices that include DNA synthesis screening protocols.76 For example, certain screening
guidelines have been implemented through industry standards and other requirements for

71 Sarah R. Carter, Jaime M. Yassif, and Christopher R. Isaac, Benchtop DNA Synthesis Devices: Capabilities,
Biosecurity Implications, and Governance, Nuclear Threat Initiative, 2023,
https://www.nti.org/analysis/articles/benchtop-dna-synthesis-devices-capabilities-biosecurity-implications-and-
governance/.
72 CRS Report R47695, U.S. Oversight of Laboratory Biosafety and Biosecurity: Current Policies, Recommended
Reforms, and Options for Congress, by Todd Kuiken.
73 Nick Bostrom, “Information Hazards: A Typology of Potential Harms from Knowledge,” Review of Contemporary
Philosophy, vol. 10 (2011).
74 Helena, Biosecurity in the Age of AI, 2023, https://www.helenabiosecurity.org/.
75 CRS Report R47695, U.S. Oversight of Laboratory Biosafety and Biosecurity: Current Policies, Recommended
Reforms, and Options for Congress, by Todd Kuiken.
76 International Gene Synthesis Consortium, “Harmonized Screening Protocol V2,” 2017,
https://genesynthesisconsortium.org/wp-content/uploads/IGSCHarmonizedProtocol11-21-17.pdf.
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federally funded research to address biosafety and biosecurity risks associated with DNA
synthesis, along with other mechanisms that may control access to certain equipment or
capabilities.
Currently, the International Gene Synthesis Consortium’s (IGSC’s) Harmonized Screening
Protocol specifies that synthetic gene sequence orders be screened against the IGSC’s Regulated
Pathogen Database, which contains sequences and organisms subject to regulatory control or
licensing.77 HHS developed its Screening Framework Guidance for Providers and Users of
Synthetic Nucleic Acids to align with “Providers’ and Customers’ existing protocols and business
practices, to be implemented without unnecessary cost, and to minimize any negative impacts on
the conduct of research and business operations.”78 On October 30, 2023, the White House
released Executive Order 14110, Safe, Secure, and Trustworthy Development and Use of Artificial
Intelligence, which requires federal agencies to “establish a framework, incorporating, as
appropriate, existing United States Government guidance, to encourage providers of synthetic
nucleic acid sequences to implement comprehensive, scalable, and verifiable synthetic nucleic
acid procurement screening mechanisms, including standards and recommended incentives.”79
Questions Congress could face in this area include whether these types of voluntary, or agency-
directed, screening programs should be applied to data that AI models are trained on, whether
additional oversight focused on AI’s use in biology and the broader scientific enterprise is
necessary, and if so, whether agencies have expertise and capacity to implement such a program,
or whether current oversight systems are sufficient.
Broad-Based vs. Case-by-Case Oversight
Some have identified the importance of distinguishing the risks associated with the use of AI
models to generate experimental procedures from the risks associated with the use of AI-enabled
biological design tools.80 They argue that doing so may be useful when exploring potential
governance options. This could prove difficult as such biology-focused AI models and biological
design tools are often developed in tandem.81 An example of this was described in an
announcement by Google Cloud and Ginkgo Bioworks, an engineering biology company, of their
plans to build a “generative AI platform for engineering biology and for biosecurity.”82 The
announcement described the convergence of biological data (which includes genomic data), AI,
and biodesign tools, in which biological data is designed and structured for use with the AI tools
and integrated into a biofoundry. A biofoundry is a facility that provides integrated infrastructure
(combination of biology, computer-aided design, robotics, and engineering principles) enabling

77 Ibid.
78 Administration for Strategic Preparedness and Response, Screening Framework Guidance for Providers and Users
of Synthetic Nucleic Acids, U.S. Department of Health & Human Services, 2023,
https://aspr.hhs.gov/legal/synna/Documents/SynNA-Guidance-2023.pdf.
79 Executive Order 14110, “Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence,” 88 Federal
Register 75191-75226 (2023).
80 Jonas B. Sandbrink, “Artificial Intelligence and Biological Misuse: Differentiating Risks of Language Models and
Biological Design Tools,” preprint, https://arxiv.org/abs/2306.13952.
81 Anna Nowogrodzki, “The Automatic-Design Tools That Are Changing Synthetic Biology,” Nature, vol. 564 (2018).
82 Ginkgo Bioworks, “Google and Ginkgo: Foundry-Scale Data Meets AI,” press release, 2023,
https://www.ginkgobioworks.com/2023/08/29/google-and-ginkgo-foundry-scale-data-meets-ai/.
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the rapid design, construction, and testing of engineered organisms for biotechnology applications
and research.83
The potential capabilities of AI used in conjunction with biological design and other types of
scientific research and experimentation have prompted recommendations on how to prevent the
misuse of AI applications in biology and science more broadly. Those recommendations include
keeping a human “in the loop,” controlling access to DNA sequences and synthesis capabilities,
and other governance mechanisms that restrict, or monitor, who can access certain AI applications
and biological design tools.84
Existing and proposed oversight mechanisms take different approaches, such as controlling
access to a particular technology or dataset, or focusing on a particular step in the R&D process
(e.g., basic research, prototypes, or release of a final product).
If further regulation or oversight in this area were deemed necessary, potential options for
Congress could include
• whether AI, and its use in biology and other R&D applications, should be
regulated broadly across all use cases and areas of development; or
• whether to design oversight mechanisms on a case-by-case basis (e.g., limiting
access to certain tools or models), focused on particular application areas (e.g.,
drug development), or based on particular end-use products (e.g., certain
laboratory equipment).
Additional regulation and oversight could impact other areas of R&D in specific or unanticipated
ways. For example, limiting access to an AI model, restricting the types of data on which an AI
model is trained, or limiting the capabilities an AI model is allowed to execute could each
individually impact a biological design tool’s capability. Limiting AI’s access to certain DNA
datasets based on particular biosecurity concerns (e.g., pathogenicity) could prevent an AI model
from designing certain toxins of concern. It might also, however, prevent the AI model from
predicting molecular structures that could enable the development of new drug candidates.
Structured Access
Some experts have called for structured access to certain AI capabilities while others suggest a
gradient approach.85 In a structured access approach, the developer of an AI model maintains
control over how the AI system can be used, modified, and reproduced.86 Structured access could
also be implemented for other research tools in the R&D process (e.g., desktop DNA

83 CRS Report R47265, Synthetic/Engineering Biology: Issues for Congress, by Todd Kuiken. Nathan Hillson, Mark
Caddick, and Yizhi Cai, et al., “Building a Global Alliance of Biofoundries,” Nature Communications, vol. 10, no.
2040 (2019).
84 Fabio Urbina, Filippa Lentzos, and Cedric Invernizzi, et al., “Preventing AI From Creating Biochemical Threats,”
Journal of Chemical Information and Modeling, vol. 63 (2023). Helena, Biosecurity in the Age of AI, 2023,
https://www.helenabiosecurity.org/.
85 Irene Solaiman, “Generative AI Systems Aren’t Just Open or Closed Source,” Wired, 2023,
https://www.wired.com/story/generative-ai-systems-arent-just-open-or-closed-source/.
86 Helena, Biosecurity in the Age of AI, 2023, https://www.helenabiosecurity.org/. Note: There are two broad categories
of structured access: (1) use controls, which govern the direct use of the AI system (who, what, when, where, why, and
how); and (2) modification and reproduction controls, which prevent the user from altering the AI system or building
their own version in a way that circumvents use controls.
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synthesizers),87 for example, by controlling the acquisition of the device itself.88 This would not
necessarily impact the AI component of the R&D process. At one university where a team of
researchers was proposing to develop an AI model to predict the toxicity of chemicals and
materials, its research ethics and society review panel suggested that the team think about ways to
control the distribution of the software, model, and outputs in order to reduce potential misuse.89
This type of review and oversight is similar to how the United States addresses some aspects of
biosafety and biosecurity, with research institutions responsible for implementation of certain
guidelines as a condition for receiving federal funding.90 Congress may examine whether federal
agencies have the expertise, capacity, and authority to both develop and issue guidance requiring
institutions, or other actors, who receive federal funding to conduct certain biosafety and
biosecurity reviews of AI enabled R&D applications. Congress may also examine whether this
type of biosafety/biosecurity review resulting in potential restricted access can be accomplished
by granting authority to agencies to conduct such reviews, regardless of whether an actor receives
federal funding or not.
Issues Pertaining to Genetic Sequence Data and Databases
The U.S. National Security Commission on Artificial Intelligence recommended that the U.S.
fund and prioritize the development of a biobank containing a “wide range of high-quality
biological and genetic data sets securely accessible by researchers.” The United States currently
has a number of such databases, including GenBank.91 However, they are distributed across
different agencies, contain different sources and types of sequence data, and are designed for
different uses.92 There are other sequence databases which are publicly funded and freely
accessible, and others that are privately held. Some experts have raised privacy and security
concerns around these databases in terms of access and potential use of the sequence data.93
Potential options for Congress in this area may include
• whether the United States should develop a sequence database strategy
coordinating activities across the U.S. government in relation to the collection,
storage, maintenance, and access to its sequence databases;
• whether certain privacy, biosafety, and biosecurity issues pertaining to access and
use of sequence databases are adequately addressed, particularly related to the
convergence of AI and biological design; and
• whether these issues should be addressed regarding sequence databases which are
publicly funded and freely accessible or privately held.

87 Sarah R. Carter, Jaime M. Yassif, and Christopher R. Isaac, Benchtop DNA Synthesis Devices: Capabilities,
Biosecurity Implications, and Governance, Nuclear Threat Initiative, 2023,
https://www.nti.org/analysis/articles/benchtop-dna-synthesis-devices-capabilities-biosecurity-implications-and-
governance/.
88 Helena, Biosecurity in the Age of AI, 2023, https://www.helenabiosecurity.org/.
89 Sadasivan Shankar and Richard N. Zare, “The Perils of Machine Learning in Designing New Chemicals and
Materials,” Nature Machine Intelligence, vol. 4 (2022).
90 CRS Report R47695, U.S. Oversight of Laboratory Biosafety and Biosecurity: Current Policies, Recommended
Reforms, and Options for Congress, by Todd Kuiken.
91 NIH, GenBank, https://www.ncbi.nlm.nih.gov/genbank/.
92 CRS In Focus IF12285, eDNA/eRNA: Scientific Value in What’s Left Behind, coordinated by Todd Kuiken.
93 Saadia Arshad, Junaid Arshad, and Muhammad Mubashir Khan, et al., “Analysis of Security and Privacy Challenges
for DNA-Genomics Applications and Databases,” Journal of Biomedical Informatics, vol. 119 (2021).
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Other Policy Considerations
A general policy consideration may be whether a broader risk management approach94 examining
the R&D system as a whole is more appropriate than addressing individual components of the
R&D process. This would include at what stage oversight is warranted (e.g., basic research,
prototyping, or prior to release of a final product) and who might conduct such oversight (e.g., by
a federal agency or through self-governance incentives). For example, would a federal agency
have the authority to conduct oversight or could oversight be achieved through self-governance
incentives? This could be similar to the operation of institutional review boards95 or other
industry-led programs such as the International Gene Synthesis Consortium,96 which conducts
screening for DNA synthesis orders.
Congressional options for addressing federal oversight could include whether to provide an
agency (or agencies) authority to conduct oversight of various aspects of AI, create a new agency
with authority over AI, or authorize agencies under current law to enforce certain oversight
responsibilities. Authorizing agencies under current law may require additional coordination
among federal agencies in order to establish oversight responsibilities. While it was established
via an executive order, this type of coordination among agencies would be similar to how the
Coordinated Framework for Regulation of Biotechnology established mechanisms for three
federal agencies (USDA, FDA, and EPA) to share responsibility for regulating the products of
biotechnology.97
Potential oversight and governance of the use of AI in the biological sciences both domestically
and internationally, either generally or focused on particular use cases or areas of development,
may have unpredicted societal and economic impacts, both positive and negative, affecting U.S.
strategic competitiveness. Such concerns could include how potential U.S. oversight and
governance influences, or is influenced by, policy actions taken elsewhere; whether international
harmonization may be needed, and, if so, through what mechanisms (e.g., the United Nations,
international standards bodies, trade policy); and how to engage in those negotiations.

Author Information

Todd Kuiken

Analyst in Science and Technology Policy

94 In January 2023 the National Institute of Standards and Technology released its AI Risk Management Framework to
better manage risks to individuals, organizations, and society associated with AI. See
https://doi.org/10.6028/NIST.AI.100-1.
95 U.S. Food and Drug Administration, Institutional Review Boards Frequently Asked Questions, 2019,
https://www.fda.gov/regulatory-information/search-fda-guidance-documents/institutional-review-boards-frequently-
asked-questions.
96 International Gene Synthesis Consortium, “Harmonized Screening Protocol V2,” 2017,
https://genesynthesisconsortium.org/wp-content/uploads/IGSCHarmonizedProtocol11-21-17.pdf.
97 Executive Office of the President (EOP), Office of Science and Technology Policy, “Coordinated Framework for
Regulation of Biotechnology,” 51 Federal Register 23302, June 26, 1986. For additional information on the
Coordinated Framework see CRS Report R46737, Agricultural Biotechnology: Overview, Regulation, and Selected
Policy Issues, by Renée Johnson.
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