Federal Register / Vol. 89, No. 4 / Friday, January 5, 2024 / Notices 783 

Æ Proposed actions that can be 

adopted to reduce the burden and cost 

of FedRAMP authorizations for 

agencies. 

• Collect information and feedback 

on agency compliance with, and 

implementation of, FedRAMP 

requirements. 

• Serve as a forum that facilitates 

communication and collaboration 

among the FedRAMP stakeholder 

community. 

The FSCAC will meet no fewer than 

three (3) times a calendar year. Meetings 

shall occur as frequently as needed, 

called, and approved by the DFO. 

Meetings may be held virtually or in 

person. Members will serve without 

compensation and may be allowed 

travel expenses, including per diem, in 

accordance with 5 U.S.C. 5703. 

The Committee shall be comprised of 

not more than 15 members who are 

qualified representatives from the 

public and private sectors, appointed by 

the Administrator, in consultation with 

the Director of OMB, as follows: 

i. The GSA Administrator or the GSA 

Administrator’s designee, who shall be 

the Chair of the Committee. 

ii. At least one representative each 

from the Cybersecurity and 

Infrastructure Security Agency and the 

National Institute of Standards and 

Technology. 

iii. At least two officials who serve as 

the Chief Information Security Officer 

within an agency, who shall be required 

to maintain such a position throughout 

the duration of their service on the 

Committee. 

iv. At least one official serving as 

Chief Procurement Officer (or 

equivalent) in an agency, who shall be 

required to maintain such a position 

throughout the duration of their service 

on the Committee. 

v. At least one individual representing 

an independent assessment organization 

vi. At least five representatives from 

unique businesses that primarily 

provide cloud computing services or 

products, including at least two 

representatives from a small business 

(as defined by section 3(a) of the Small 

Business Act (15 U.S.C. 632(a))). 

vii. At least two other representatives 

from the Federal Government as the 

Administrator determines to be 

necessary to provide sufficient balance, 

insights, or expertise to the Committee. 

Each member shall be appointed for a 

term of three (3) years, except the initial 

terms, which were staggered into one 

(1), two (2) or three (3) year terms to 

establish a rotation in which one third 

of the members are selected. No member 

shall be appointed for more than two (2) 

consecutive terms nor shall any member 

serve for more than six (6) consecutive 

years. GSA values opportunities to 

increase diversity, equity, inclusion and 

accessibility on its federal advisory 

committees. 

Members will be designated as 

Regular Government Employees (RGEs) 

or Representative members as 

appropriate and consistent with Section 

3616(d) of the FedRAMP Authorization 

Act of 2022. GSA’s Office of General 

Counsel will assist the Designated 

Federal Officer (DFO) to determine the 

advisory committee member 

designations. Representatives are 

members selected to represent a specific 

point of view held by a particular group, 

organization, or association. Members 

who are full time or permanent parttime Federal civilian officers or 

employees shall be appointed to serve 

as Regular Government Employee (RGE) 

members. In accordance with OMB 

Final Guidance published in the 

Federal Register on October 5, 2011 and 

revised on August 13, 2014, federally 

registered lobbyists may not serve on 

the Committee in an individual capacity 

to provide their own individual best 

judgment and expertise, such as RGEs 

members. This ban does not apply to 

lobbyists appointed to provide the 

Committee with the views of a 

particular group, organization, or 

association, such as Representative 

members. 

Applications 

Applications are being accepted to fill 

the remaining terms of two vacant seats 

and to fill two seats with upcoming 

expiring terms. These four seats will be 

designated as Representative members: 

Two (2) seats for representatives of a 

unique business that primarily provides 

cloud computing products or services. 

One seat will be appointed to serve for 

the remainder of the vacant term, 

scheduled to end in May 2025, and the 

other will be appointed for a three year 

term. 

Two (2) seats for representatives of a 

unique business that primarily provides 

cloud computing products or services 

from a small business (as defined by 

section 3(a) of the Small Business Act 

(15 U.S.C. 632(a))). One seat will be 

appointed to serve for the remainder of 

the vacant term, scheduled to end in 

July 2026, and the other will be 

appointed for a three year term. 

Applications for membership on the 

Committee will be accepted until 5:00 

p.m. Eastern Standard Time on Monday, 

January 22, 2024. 

There are two parts to submitting an 

application. First, complete the 

information requested via this electronic 

form https://forms.gle/

kxscdjX6P7oB9vua7. Next, email your 

CV or resume and a letter of 

endorsement from your organization or 

organization’s leadership, endorsing you 

to represent your company, to fscac@

gsa.gov with the subject line: FSCAC 

APPLICATION—[Applicant Name]. The 

letter of endorsement must come from 

your organization or organization’s 

leadership. If you are the CEO, then it 

must come from another member of the 

executive team of your organization, as 

you cannot endorse yourself. The letter 

must be signed and specifically state 

that you are authorized to apply to 

FSCAC as a representative of your 

organization. 

Please note: Letters of 

‘‘recommendation’’ or other unsolicited 

deliverables will neither be accepted 

nor acknowledged. Do not include 

them. 

Applications that do not include the 

completion of the above instructions 

will not be considered. 

Elizabeth Blake, 

Senior Advisor, Federal Acquisition Service, 

General Services Administration. 

[FR Doc. 2023–28602 Filed 1–4–24; 8:45 am] 

BILLING CODE 6820–34–P 

GENERAL SERVICES 

ADMINISTRATION 

[Notice–MY–2023–03; Docket No. 2023– 

0002; Sequence No. 37] 

Office of Shared Solutions and 

Performance Improvement (OSSPI); 

Chief Data Officers Council (CDO); 

Request for Information—Synthetic 

Data Generation 

AGENCY: Federal Chief Data Officers 

(CDO) Council; General Services 

Administration, (GSA). 

ACTION: Notice. 

SUMMARY: The Federal CDO Council was 

established by the Foundations for 

Evidence-Based Policymaking Act. The 

Council’s vision is to improve 

government mission achievement and 

increase benefits to the nation through 

improving the management, use, 

protection, dissemination, and 

generation of data in government 

decision-making and operations. The 

CDO Council is publishing this Request 

for Information (RFI) for the public to 

provide input on key questions 

concerning synthetic data generation. 

Responses to this RFI will inform the 

CDO Council’s work to establish best 

practices for synthetic data generation. 

DATES: We will consider comments 

received by February 5, 2024. 


lotter on DSK11XQN23PROD with NOTICES1
784 Federal Register / Vol. 89, No. 4 / Friday, January 5, 2024 / Notices 

1H.R. 4174—115th Congress (2017–2018): 

Foundations for Evidence-Based Policymaking Act 

of 2018 | Congress.gov | Library of Congress https:// 

www.congress.gov/bill/115th-congress/house-bill/ 

4174/text. 

2 44 U.S.C. 3502(16). 

3 44 U.S.C. 3502(17). 

4 44 U.S.C. 3502(20). 

5https://csrc.nist.gov/glossary/term/synthetic_

data_generation. 

6 15 U.S.C. 9204. 

7A useful definition of this technique is available 

in the abstract of this paper: https://www.ncbi.

nlm.nih.gov/pmc/articles/PMC8774760/. 

8This technique is described in the Department 

of Defense DevSecOps Fundamentals Guidebook 

https://dodcio.defense.gov/Portals/0/Documents/ 

Library/DevSecOpsTools-ActivitiesGuidebook.pdf, 

page 23. 

9NIST Special Publication 800–188, Section 4.4 

https://doi.org/10.6028/NIST.SP.800-188. 

Targeted Audience 

This RFI is intended for Chief Data 

Officers, data scientists, technologists, 

data stewards and data- and evidencebuilding related subject matter experts 

from the public, private, and academic 

sectors. 

ADDRESSES: Respondents should submit 

comments identified by Notice–MY– 

2023–03 via the Federal eRulemaking 

Portal at https://www.regulations.gov 

and follow the instructions for 

submitting comments. All public 

comments received are subject to the 

Freedom of Information Act and will be 

posted in their entirety at 

regulations.gov, including any personal 

and/or business confidential 

information provided. Do not include 

any information you would not like to 

be made publicly available. 

Written responses should not exceed 

six pages, inclusive of a one-page cover 

page as described below. Please respond 

concisely, in plain language, and specify 

which question(s) you are responding 

to. You may also include links to online 

materials or interactive presentations, 

but please ensure all links are publicly 

available. Each response should 

include: 

• The name of the individual(s) and/ 

or organization responding. 

• A brief description of the 

responding individual(s) or 

organization’s mission and/or areas of 

expertise. 

• The section(s) that your submission 

and materials are related to. 

• A contact for questions or other 

follow-up on your response. 

By responding to the RFI, each 

participant (individual, team, or legal 

entity) warrants that they are the sole 

author or owner of, or has the right to 

use, any copyrightable works that the 

submission comprises, that the works 

are wholly original (or is an improved 

version of an existing work that the 

participant has sufficient rights to use 

and improve), and that the submission 

does not infringe any copyright or any 

other rights of any third party of which 

participant is aware. 

By responding to the RFI, each 

participant (individual, team, or legal 

entity) consents to the contents of their 

submission being made available to all 

Federal agencies and their employees on 

an internal-to-government website 

accessible only to agency staff persons. 

Participants will not be required to 

transfer their intellectual property rights 

to the CDO Council, but participants 

must grant to the Federal Government a 

nonexclusive license to apply, share, 

and use the materials that are included 

in the submission. To participate in the 

RFI, each participant must warrant that 

there are no legal obstacles to providing 

the above-referenced nonexclusive 

licenses of participant rights to the 

Federal Government. Interested parties 

who respond to this RFI may be 

contacted for follow-on questions or 

discussion. 

FOR FURTHER INFORMATION CONTACT: 

Issues regarding submission or 

questions can be sent to Ken Ambrose 

and Ashley Jackson, Senior Advisors, 

Office of Shared Solutions and 

Performance Improvement, General 

Services Administration, at 202–215– 

7330 (Kenneth Ambrose) and 202–538– 

2897 (Ashley Jackson), or cdocstaff@

gsa.gov. 

SUPPLEMENTARY INFORMATION: 

Background 

Pursuant to the Foundations for 

Evidence-Based Policy Making Act of 

2018,1 the CDO Council is charged with 

establishing best practices for the use, 

protection, dissemination, and 

generation of data in the Federal 

Government. In reviewing existing 

activities and literature from across the 

Federal Government, the CDO Council 

has determined that: 

• the Federal Government would 

benefit from developing consensus of a 

more formalized definition for synthetic 

data generation, 

• synthetic data generation has wideranging applications, and 

• there are challenges and limitations 

with synthetic data generation. 

The CDO council is interested in 

consolidating feedback and inputs from 

qualified experts to gain additional 

insight and assist with establishing a 

best practice guide around synthetic 

data generation. The CDO Council has 

preliminarily drafted a working 

definition of synthetic data generation 

and several key questions to better 

inform its work. 

Information and Key Questions 

Section 1: Defining Synthetic Data 

Generation 

Synthetic data generation is an 

important part of modern data science 

work. In the broadest sense, synthetic 

data generation involves the creation of 

a new synthetic or artificial dataset 

using computational methods. Synthetic 

data generation can be contrasted with 

real-world data collection. Real-world 

data collection involves gathering data 

from a first-hand source, such as 

through surveys, observations, 

interviews, forms, and other methods. 

Synthetic data generation is a broad 

field that employs varied techniques 

and can be applied to many different 

kinds of problems. Data may be fully or 

partially synthetic. A fully synthetic 

dataset wholly consists of points created 

using computational methods, whereas 

a partially synthetic dataset may involve 

a mix of first-hand and computationally 

generated synthetic data. 

Throughout this RFI, we use the 

following definitions: 

• data—recorded information, 

regardless of form or the media on 

which the data is recorded; 2

• data asset—a collection of data 

elements or data sets that may be 

grouped together; 3

• open government data asset—a 

public data asset that is (A) machinereadable; (B) available (or could be 

made available) in an open format; (C) 

not encumbered by restrictions, other 

than intellectual property rights, 

including under titles 17 and 35, that 

would impede the use or reuse of such 

asset; and (D) based on an underlying 

open standard that is maintained by a 

standards organization.4

The National Institute of Standards 

and Technology (NIST) defines 

synthetic data generation as ‘‘a process 

in which seed data is used to create 

artificial data that has some of the 

statistical characteristics as the seed 

data’’.5

The CDO Council believes that this 

definition of synthetic data generation 

includes techniques such as using 

statistics to create data from a known 

distribution, generative adversarial 

networks (GANs),6 variational 

autoencoding (VAE),7 building test data 

for use in software development,8

privacy-preserving synthetic data 

generation 9 and others. 

The CDO Council also believes that it 

is important to draw contrasts between 

synthetic data generation and other 

activities. For example, synthetic data 

generation does not include collection 


lotter on DSK11XQN23PROD with NOTICES1
Federal Register / Vol. 89, No. 4 / Friday, January 5, 2024 / Notices 785 

10This application is briefly described at https:// 

frederick.cancer.gov/initiatives/scientific-standardshub/ai-and-data-science, Section 4. 

11A definition of this technique is available in the 

abstract of this paper https://par.nsf.gov/servlets/ 

purl/10187206. 

12A definition a computer simulation is proposed 

at https://builtin.com/hardware/computersimulation. 

13 DoD DevSecOps Fundamentals, ibid. 

14OMB Circular A–130, Appendix II https://

www.whitehouse.gov/wp-content/uploads/legacy_

drupal_files/omb/circulars/A130/a130revised.pdf. 

of data without any inference. Synthetic 

data generation does not include signal 

processing, such as automated 

differential translations of global 

positioning satellite data. Synthetic data 

generation also does not include 

enriching data during data analysis— 

such intermediate steps that involve 

augmenting or enhancing existing data 

but do not involve the creation of 

artificial data. 

Other analysis techniques, such as 

distribution fitting and parametric 

modeling, are closely related to 

synthetic data generation. The CDO 

Council believes the key difference; 

however, is the purpose of the 

computational methods. Synthetic data 

generation seeks to create wholly new 

data points based on the statistical 

properties of a dataset, whereas 

distribution fitting seeks to ‘fill in’ a 

dataset based on a known distribution. 

Notably, the fitted distribution can be 

used to generate points that are not part 

of the original dataset—which is an 

application of synthetic data generation. 

Questions 

• Are there any limitations to relying 

on the NIST definition to describe the 

field of synthetic data generation? How 

should it be improved? 

• How well does the CDO Council’s 

list of examples and contrasts improve 

understanding? How should these be 

improved? 

Section 2: Applying Synthetic Data 

Generation 

Synthetic data generation can enable 

the creation of larger and more diverse 

datasets, enhance model performance, 

and protect individual privacy. The 

CDO Council’s review of potential 

applications of synthetic data generation 

found examples in: 

• Data augmentation.10 This 

application involves creating new data 

points or datasets from existing data. 

This application can be particularly 

useful in developing training datasets 

for machine learning and advanced 

analytics. 

• Data synthesis.11 This application 

involves using an existing dataset to 

create a new dataset, sharing similar 

statistical properties with the original 

dataset, to protect individual privacy. 

Generating such datasets has wideranging applications including, but not 

limited to, facilitating reproducible 

investigation of clinical data while 

preserving individual privacy. 

• Modeling and simulation.12 This 

application involves setting 

assumptions, parameters and rules to 

develop data for further analysis. The 

synthetic dataset can be used for 

developing insights, testing hypotheses, 

and/or understanding a model’s 

behavior. This application supports the 

conduct of controlled experiments, 

predicting potential future outcomes 

from current conditions, generating 

scenarios for rare or extreme events, and 

validating or calibrating a model. 

• Software development.13 This 

application involves using existing 

database schemas to simulate real-world 

scenarios and ensure that a software 

application can handle different types of 

data and errors effectively. This 

application assists in the creation of 

representative data, makes it easier to 

generate edge cases, protects individual 

privacy, and improves testing efficiency. 

Notably, the CDO Council believes 

that not all applications of modeling 

and simulation would meet the 

definition of synthetic data generation. 

For example, weather forecasting 

applies numerical models and applies a 

complex mix of data analysis, 

meteorological science, and 

computation methods but does not 

involve the creation of synthetic or 

artificial data points. Instead, the 

purpose of these models is to predict 

future conditions. 

Questions 

• How are these examples 

representative of synthetic data 

generation? How should they be 

revised? 

• What other examples of synthetic 

data generation should the CDO Council 

know about? 

• What are the key advantages for the 

use of synthetic data generation? 

Section 3: Challenges and Limitations in 

Synthetic Data Generation 

The CDO Council recognizes that 

synthetic data generation can be a 

valuable technique. However, it should 

be noted that there are some challenges 

and limitations with the technique. For 

example, there can be challenges 

generating data that realistically 

simulates the real world and the 

diversity of real data. Additionally, 

evaluating the quality of a synthetic 

dataset may also be extremely 

challenging. 

Synthetic data generation is also 

subject to challenges commonly facing 

any statistical methods, such as 

overfitting and imbalances in the source 

data. These challenges reduce the utility 

of the generated synthetic data because 

they may not be properly representative, 

including failing to represent rare 

classes. 

Questions 

• What other challenges and 

limitations are important to consider in 

synthetic data generation? 

• What tools or techniques are 

available for effectively communicating 

the limitations of generated synthetic 

data? 

• What are best practices for CDOs to 

coordinate with statistical officials on 

synthetic data? 

• What approaches can CDOs 

consider to help address these 

challenges? 

Section 4: Ethics and Equity 

Considerations in Synthetic Data 

Generation 

Synthetic data generation techniques 

hold great promise, but also introduce 

questions of ethics and equity. 

Consistent with Federal privacy 

practices,14 any data generation 

technique involving individuals must 

respect their privacy rights and obtain 

informed consent before using realworld data to generate synthetic data. 

As noted in Section 3, synthetic data 

generation is also subject to challenges 

commonly facing any statistical 

methods and has the potential to 

introduce and encode errors or bias, 

potentially leading to discriminatory 

outcomes. 

Uses of generated synthetic data must 

also be carefully considered. The 

context and quality of the generated 

synthetic data will impact its practical 

utility and impact. Assessing and 

understanding the fitness of a generated 

synthetic dataset is essential. For 

instance, a generated synthetic dataset 

may not sufficiently represent the 

diversity of the source dataset. In 

addition, a generated synthetic dataset 

may not contain sufficient variables to 

fully represent the system and the 

drivers of differences in the 

phenomenon it is meant to represent. 

Questions 

• What techniques are available to 

facilitate transparency around generated 

synthetic data? 

• What are best practices for CDOs to 

coordinate with privacy officials on 


lotter on DSK11XQN23PROD with NOTICES1
786 Federal Register / Vol. 89, No. 4 / Friday, January 5, 2024 / Notices 

15https://resources.data.gov/assets/documents/ 

fds-data-ethics-framework.pdf. 

16OMB Memorandum M–19–23. 

17 44 U.S.C. 3520(20). 

18OMB Memorandum M–19–23, Appendix A. 

19OMB Memorandum M–19–23, Section II (Key 

Senior Officials). 

ethics and equity matters related to 

synthetic data generation? 

• How can we apply the Federal Data 

Ethics Framework 15 to address these 

ethics and equity concerns? 

Section 5: Synthetic Data Generation 

and Evidence-Building 

Synthetic data generation can enable 

the production of evidence for use in 

policymaking. Applications such as 

simulation or modeling can help 

policymakers explore scenarios and 

their potential impacts. Likewise, 

policymakers can conduct controlled 

experiments of potential policy 

interventions to better understand their 

impacts. Data synthesis may help 

policymakers make more data publicly 

available to spur research and other 

foundational fact-finding activities that 

can inform policymaking. 

Questions 

• What other applications of 

synthetic data generation support 

evidence-based policymaking? 16

• What is the relationship between 

synthetic data generation and open 

government data? 17

• How can CDOs and Evaluation 

Officers best collaborate on synthetic 

data generation to support evidencebuilding? 18 What about other evidence 

officials? 19

Kenneth Ambrose, 

Senior Advisor CDO Council, Office of Shared 

Solutions and Performance Improvement, 

General Services Administration. 

[FR Doc. 2024–00036 Filed 1–4–24; 8:45 am] 

BILLING CODE 6820–69–P 

DEPARTMENT OF DEFENSE 

GENERAL SERVICES 

ADMINISTRATION 

NATIONAL AERONAUTICS AND 

SPACE ADMINISTRATION 

[OMB Control No. 9000–0064; Docket No. 

2024–0053; Sequence No. 1] 

Information Collection; Certain Federal 

Acquisition Regulation Part 36 

Construction Contract Requirements 

AGENCY: Department of Defense (DOD), 

General Services Administration (GSA), 

and National Aeronautics and Space 

Administration (NASA). 

ACTION: Notice and request for 

comments. 

SUMMARY: In accordance with the 

Paperwork Reduction Act of 1995, and 

the Office of Management and Budget 

(OMB) regulations, DoD, GSA, and 

NASA invite the public to comment on 

a revision concerning certain Federal 

Acquisition Regulation part 36 

construction contract requirements. 

DoD, GSA, and NASA invite comments 

on: whether the proposed collection of 

information is necessary for the proper 

performance of the functions of Federal 

Government acquisitions, including 

whether the information will have 

practical utility; the accuracy of the 

estimate of the burden of the proposed 

information collection; ways to enhance 

the quality, utility, and clarity of the 

information to be collected; and ways to 

minimize the burden of the information 

collection on respondents, including the 

use of automated collection techniques 

or other forms of information 

technology. OMB has approved this 

information collection for use through 

April 30, 2024. DoD, GSA, and NASA 

propose that OMB extend its approval 

for use for three additional years beyond 

the current expiration date. 

DATES: DoD, GSA, and NASA will 

consider all comments received by 

March 5, 2024. 

ADDRESSES: DoD, GSA, and NASA 

invite interested persons to submit 

comments on this collection through 

https://www.regulations.gov and follow 

the instructions on the site. This website 

provides the ability to type short 

comments directly into the comment 

field or attach a file for lengthier 

comments. If there are difficulties 

submitting comments, contact the GSA 

Regulatory Secretariat Division at 202– 

501–4755 or GSARegSec@gsa.gov. 

Instructions: All items submitted 

must cite OMB Control No. 9000–0064, 

Certain Federal Acquisition Regulation 

Part 36 Construction Contract 

Requirements. Comments received 

generally will be posted without change 

to https://www.regulations.gov, 

including any personal and/or business 

confidential information provided. To 

confirm receipt of your comment(s), 

please check www.regulations.gov, 

approximately two-to-three days after 

submission to verify posting. 

FOR FURTHER INFORMATION CONTACT: 

Zenaida Delgado, Procurement Analyst, 

at telephone 202–969–7207, or 

zenaida.delgado@gsa.gov. 

SUPPLEMENTARY INFORMATION: 

A. OMB Control Number, Title, and 

Any Associated Form(s) 

9000–0064, Certain Federal 

Acquisition Regulation Part 36 

Construction Contract Requirements. 

B. Need and Uses 

DoD, GSA, and NASA are combining 

OMB Control Nos. by FAR part. This 

consolidation is expected to improve 

industry’s ability to easily and 

efficiently identify burdens associated 

with a given FAR part. This review of 

the information collections by FAR part 

allows improved oversight to ensure 

there is no redundant or unaccounted 

for burden placed on industry. Lastly, 

combining information collections in a 

given FAR part is also expected to 

reduce the administrative burden 

associated with processing multiple 

information collections. 

This justification supports the 

extension of OMB Control No. 9000– 

0064 and combines it with the 

previously approved information 

collection under OMB Control No. 

9000–0062, with the new title ‘‘Certain 

Federal Acquisition Regulation Part 36 

Construction Contract Requirements’’. 

Upon approval of this consolidated 

information collection, OMB Control 

No. 9000–0062 will be discontinued. 

The burden requirements previously 

approved under the discontinued 

number will be covered under OMB 

Control No. 9000–0064. 

This clearance covers the information 

that contractors must submit to comply 

with the following FAR requirements: 

• FAR 52.236–5, Material and 

Workmanship. This clause requires 

contractors to obtain contracting officer 

approval of the machinery, equipment, 

material, or articles to be incorporated 

into the work. The contractor’s request 

must include: the manufacturer’s name, 

the model number, and other 

information concerning the 

performance, capacity, nature, and 

rating of the machinery and mechanical 

and other equipment; and full 

information concerning the material or 

articles. When directed by the 

contracting officer, the contractor must 

submit samples of the items requiring 

approval for incorporating into the 

work. The contracting officer uses this 

information to determine whether the 

machinery, equipment, material, or 

articles meet the standards of quality 

specified in the contract. A contracting 

officer may reject work, if the contractor 

installs machinery, equipment, material, 

or articles in the work without obtaining 

the contracting officer’s approval. 

• FAR 52.236–13, Accident 

Prevention, Alternate I. This alternate to 


lotter on DSK11XQN23PROD with NOTICES1