40810 Federal Register / Vol. 86, No. 143 / Thursday, July 29, 2021 / Notices 

15See Order. 

not participating in this review, the cash 

deposit will continue to be the 

company-specific rate published for the 

most recently completed segment of this 

proceeding; (3) if the exporter is not a 

firm covered in this review, or the 

original less-than-fair-value (LTFV) 

investigation, but the manufacturer is, 

then the cash deposit rate will be the 

rate established for the most recent 

segment for the manufacturer of the 

merchandise; and (4) the cash deposit 

rate for all other manufacturers or 

exporters will continue to be 20.33 

percent,15 the all-others rate established 

in the LTFV investigation. These 

deposit requirements, when imposed, 

shall remain in effect until further 

notice. 

Notification to Importers 

This notice serves as a final reminder 

to importers of their responsibility 

under 19 CFR 351.402(f)(2) to file a 

certificate regarding the reimbursement 

of antidumping duties prior to 

liquidation of the relevant entries 

during this POR. Failure to comply with 

this requirement could result in 

Commerce’s presumption that 

reimbursement of antidumping duties 

occurred and the subsequent assessment 

of double antidumping duties. 

Notification Regarding Administrative 

Protective Order 

This notice serves as the only 

reminder to parties subject to 

administrative protective order (APO) of 

their responsibility concerning the 

disposition of proprietary information 

disclosed under APO in accordance 

with 19 CFR 351.305(a)(3), which 

continues to govern business 

proprietary information in this segment 

of the proceeding. Timely written 

notification of the return or destruction 

of APO materials or conversion to 

judicial protective order is hereby 

requested. Failure to comply with the 

regulations and the terms of an APO is 

a sanctionable violation. 

Notification to Interested Parties 

Commerce is issuing and publishing 

this notice in accordance with sections 

751(a)(1) and 777(i)(1) of the Act, and 19 

CFR 351.221(b)(4). 

Dated: July 23, 2021. 

Christian Marsh, 

Acting Assistant Secretary for Enforcement 

and Compliance. 

Appendix 

List of Topics Discussed in the Issues and 

Decision Memorandum 

I. Summary 

II. Background 

III. Scope of the Order 

IV. Changes Since the Preliminary Results 

V. Rate for Non-Examined Company 

VI. Discussion of the Issues 

Comment 1–A: Lawfulness of Commerce’s 

Interpretation of the Particular Market 

Situation (PMS) Provision 

Comment 1–B: Evidence of a PMS 

Comment 1–C: Quantification of PMS 

Adjustment 

Comment 2: Constructed Export Price 

(CEP) Offset for POSCO 

Comment 3: Correction of Calculation 

Errors 

Comment 4: Whether Hyundai’s Cost 

Accounting Merits Adverse Facts 

Available (AFA) 

Comment 5: Assignment of an Assessment 

Rate to a Certain U.S. Affiliate 

VII. Recommendation 

[FR Doc. 2021–16172 Filed 7–28–21; 8:45 am] 

BILLING CODE 3510–DS–P 

DEPARTMENT OF COMMERCE 

National Institute of Standards and 

Technology 

[Docket Number: [210726–0151]] 

Artificial Intelligence Risk Management 

Framework 

AGENCY: National Institute of Standards 

and Technology, Department of 

Commerce. 

ACTION: Request for information. 

SUMMARY: The National Institute of 

Standards and Technology (NIST) is 

developing a framework that can be 

used to improve the management of 

risks to individuals, organizations, and 

society associated with artificial 

intelligence (AI). The NIST Artificial 

Intelligence Risk Management 

Framework (AI RMF or Framework) is 

intended for voluntary use and to 

improve the ability to incorporate 

trustworthiness considerations into the 

design, development, and use, and 

evaluation of AI products, services, and 

systems. This notice requests 

information to help inform, refine, and 

guide the development of the AI RMF. 

The Framework will be developed 

through a consensus-driven, open, and 

collaborative process that will include 

public workshops and other 

opportunities for stakeholders to 

provide input. 

DATES: Comments in response to this 

notice must be received by 5:00 p.m. 

Eastern time on August 19, 2021. 

Written comments in response to the 

RFI should be submitted according to 

the instructions in the ADDRESSES and 

SUPPLEMENTARY INFORMATION sections 

below. Submissions received after that 

date may not be considered. 

ADDRESSES: Comments may be 

submitted by any of the following 

methods: 

• Electronic submission: Submit 

electronic public comments via the 

Federal e-Rulemaking Portal. 

1. Go to www.regulations.gov and 

enter NIST–2021–0004 in the search 

field, 

2. Click the ‘‘Comment Now!’’ icon, 

complete the required fields, and 

3. Enter or attach your comments. 

• Email: Comments in electronic form 

may also be sent to AIframework@

nist.gov in any of the following formats: 

HTML; ASCII; Word; RTF; or PDF. 

Please submit comments only and 

include your name, organization’s name 

(if any), and cite ‘‘AI Risk Management 

Framework’’ in all correspondence. 

FOR FURTHER INFORMATION CONTACT: For 

questions about this RFI contact: Mark 

Przybocki (mark.przybocki@nist.gov), 

U.S. National Institute of Standards and 

Technology, MS 20899, 100 Bureau 

Drive, Gaithersburg, MD 20899, 

telephone (301) 975–3347, email 

AIframework@nist.gov. 

Direct media inquiries to NIST’s 

Office of Public Affairs at (301) 975– 

2762. Users of telecommunication 

devices for the deaf, or a text telephone, 

may call the Federal Relay Service, toll 

free at 1–800–877–8339. 

Accessible Format: On request to the 

contact person listed above, NIST will 

make the RFI available in alternate 

formats, such as Braille or large print, 

upon request by persons with 

disabilities. 

SUPPLEMENTARY INFORMATION: 

Genesis for Development of the AI Risk 

Management Framework 

Artificial intelligence (AI) is rapidly 

transforming our world. 

Surges in AI capabilities have led to 

a wide range of innovations. These new 

AI-enabled systems are benefitting many 

parts of society and economy from 

commerce and healthcare to 

transportation and cybersecurity. At the 

same time, new AI-based technologies, 

products, and services bring technical 

and societal challenges and risks, 

including ensuring that AI comports 

with ethical values. While there is no 

objective standard for ethical values, as 

they are grounded in the norms and 

legal expectations of specific societies or 

cultures, it is widely agreed that AI 

must be designed, developed, used, and 

evaluated in a trustworthy and 

responsible manner to foster public 

confidence and trust. Trust is 

established by ensuring that AI systems 

are cognizant of and are built to align 

with core values in society, and in ways 


jbell on DSKJLSW7X2PROD with NOTICES
Federal Register / Vol. 86, No. 143 / Thursday, July 29, 2021 / Notices 40811 

1National Security Commission on Artificial 

Intelligence, Final Report, https://www.nscai.gov/ 

wp-content/uploads/2021/03/Full-Report-Digital1.pdf. 

2Plan for Federal Engagement in Developing AI 

Technical Standards and Related Tools, https://

www.nist.gov/system/files/documents/2019/08/10/ 

ai_standards_fedengagement_plan_9aug2019.pdf. 

3H. Rept. 116–455—COMMERCE, JUSTICE, 

SCIENCE, AND RELATED AGENCIES 

APPROPRIATIONS BILL, 2021, CRPT– 

116hrpt455.pdf (congress.gov), and Section 5301 of 

the National Artificial Intelligence Initiative Act of 

2020 (Pub. L. 116–283), https://www.congress.gov/ 

116/bills/hr6395/BILLS-116hr6395enr.pdf. 

4Framework for Improving Critical Infrastructure 

Cybersecurity (‘‘Cybersecurity Framework’’), 

https://www.nist.gov/cyberframework. 

5Privacy Framework: A Tool for Improving 

Privacy through Enterprise Risk Management 

(‘‘Privacy Framework’’), https://www.nist.gov/ 

privacy-framework/privacy-framework. 

which minimize harms to individuals, 

groups, communities, and societies at 

large. 

Defining trustworthiness in 

meaningful, actionable, and testable 

ways remains a work in progress. Inside 

and outside the United States there are 

diverse views about what that entails, 

including who is responsible for 

instilling trustworthiness during the 

stages of design, development,use, and 

evaluation. There also are different 

ideas about how to assure conformity 

with principles and characteristics of AI 

trustworthiness. 

NIST is among the institutions 

addressing these issues. NIST aims to 

cultivate the public’s trust in the design, 

development, use, and evaluation of AI 

technologies and systems in ways that 

enhance economic security, and 

improve quality of life. NIST focuses on 

improving measurement science, 

standards, technology, and related tools, 

including evaluation and data. NIST is 

developing forward-thinking 

approaches that support innovation and 

confidence in AI systems. The agency’s 

work on an AI RMF is consistent with 

recommendations by the National 

Security Commission on Artificial 

Intelligence 1 and the Plan for Federal 

Engagement in Developing AI Technical 

Standards and Related Tools.2

Congress has directed NIST to 

collaborate with the private and public 

sectors to develop a voluntary AI RMF.3

The Framework is intended to help 

designers, developers, users and 

evaluators of AI systems better manage 

risks across the AI lifecycle. For 

purposes of this RFI, ‘‘managing’’ 

means: Identifying, assessing, 

responding to, and communicating AI 

risks. ‘‘Responding’’ to AI risks means: 

Avoiding, mitigating, sharing, 

transferring, or accepting risk. 

‘‘Communicating’’ AI risk means: 

Disclosing and negotiating risk and 

sharing with connected systems and 

actors in the domain of design, 

deployment and use. ‘‘Design, 

development, use, and evaluation’’ of AI 

systems includes procurement, 

monitoring, or sustainment of AI 

components and systems. 

The Framework aims to foster the 

development of innovative approaches 

to address characteristics of 

trustworthiness including accuracy, 

explainability and interpretability, 

reliability, privacy, robustness, safety, 

security (resilience), and mitigation of 

unintended and/or harmful bias, as well 

as of harmful uses. The Framework 

should consider and encompass 

principles such as transparency, 

fairness, and accountability during 

design, deployment, use, and evaluation 

of AI technologies and systems. With 

broad and complex uses of AI, the 

Framework should consider risks from 

unintentional, unanticipated, or harmful 

outcomes that arise from intended uses, 

secondary uses, and misuses of the AI. 

These characteristics and principles are 

generally considered as contributing to 

the trustworthiness of AI technologies 

and systems, products, and services. 

NIST is interested in whether 

stakeholders define or use other 

characteristics and principles. 

Among other purposes, the AI RMF is 

intended to be a tool that would 

complement and assist with broader 

aspects of enterprise risk management 

which could affect individuals, groups, 

organizations, or society. 

AI RMF Development and Attributes 

NIST is soliciting input from all 

interested stakeholders, seeking to 

understand how individuals, groups 

and organizations involved with 

designing, developing, using, or 

evaluating AI systems might be better 

able to address the full scope of AI risk 

and how a framework for managing AI 

risks might be constructed. Stakeholders 

include but are not limited to industry, 

civil society groups, academic 

institutions, federal agencies, state, 

local, territorial, tribal, and foreign 

governments, standards developing 

organizations and researchers. 

NIST intends the Framework to 

provide a prioritized, flexible, riskbased, outcome-focused, and costeffective approach that is useful to the 

community of AI designers, developers, 

users, evaluators, and other decision 

makers and is likely to be widely 

adopted. The Framework’s development 

process will involve several iterations to 

encourage robust and continuing 

engagement and collaboration with 

interested stakeholders. This will 

include open, public workshops, along 

with other forms of outreach and 

feedback. This RFI is an important part 

of that process. 

NIST believes that the AI RMF should 

have the following attributes: 

1. Be consensus-driven and developed 

and regularly updated through an open, 

transparent process. All stakeholders 

should have the opportunity to 

contribute to the Framework’s 

development. NIST has a long track 

record of successfully and 

collaboratively working with a range of 

stakeholders to develop standards and 

guidelines. NIST will model its 

approach on the open, transparent, and 

collaborative approaches used to 

develop the Framework for Improving 

Critical Infrastructure Cybersecurity 

(‘‘Cybersecurity Framework’’) 4 as well 

as the Privacy Framework: A Tool for 

Improving Privacy through Enterprise 

Risk Management (‘‘Privacy 

Framework’’).5

2. Provide common definitions. The 

Framework should provide definitions 

and characterizations for aspects of AI 

risk and trustworthiness that are 

common and relevant across all sectors. 

The Framework should establish 

common AI risk taxonomy, terminology, 

and agreed-upon definitions, including 

that of trust and trustworthiness. 

3. Use plain language that is 

understandable by a broad audience, 

including senior executives and those 

who are not AI professionals, while still 

of sufficient technical depth to be useful 

to practitioners across many domains. 

4. Be adaptable to many different 

organizations, AI technologies, lifecycle 

phases, sectors, and uses. The 

Framework should be scalable to 

organizations of all sizes, public or 

private, in any sector, and operating 

within or across domestic borders. It 

should be platform- and technologyagnostic and customizable. It should 

meet the needs of AI designers, 

developers, users, and evaluators alike. 

5. Be risk-based, outcome-focused, 

voluntary, and non-prescriptive. The 

Framework should focus on the value of 

trustworthiness and related needs, 

capabilities, and outcomes. It should 

provide a catalog of outcomes and 

approaches to be used voluntarily, 

rather than a set of one-size-fits-all 

requirements, in order to: Foster 

innovation in design, development, use 

and evaluation of trustworthy and 

responsible AI systems; inform 

education and workforce development; 

and promote research on and adoption 

of effective solutions. The Framework 

should assist those designing, 

developing, using, and evaluating AI to 


jbell on DSKJLSW7X2PROD with NOTICES
40812 Federal Register / Vol. 86, No. 143 / Thursday, July 29, 2021 / Notices 

better manage AI risks for their intended 

use cases or scenarios. 

6. Be readily usable as part of any 

enterprise’s broader risk management 

strategy and processes. 

7. Be consistent, to the extent 

possible, with other approaches to 

managing AI risk. The Framework 

should, when possible, take advantage 

of and provide greater awareness of 

existing standards, guidelines, best 

practices, methodologies, and tools for 

managing AI risks whether presented as 

frameworks or in other formats. It 

should be law- and regulation-agnostic 

to support organizations’ ability to 

operate under applicable domestic and 

international legal or regulatory regimes. 

8. Be a living document. The 

Framework should be capable of being 

readily updated as technology, 

understanding, and approaches to AI 

trustworthiness and uses of AI change 

and as stakeholders learn from 

implementing AI risk management. 

NIST expects there may be aspects of AI 

trustworthiness that are not sufficiently 

developed for inclusion in the initial 

Framework. 

As noted below, NIST solicits 

comments on these and potentially 

other desired attributes of an AI RMF, 

as well as on high-priority gaps in 

organizations’ ability to manage AI 

risks. 

Goals of This Request for Information 

(RFI) 

This RFI invites stakeholders to 

submit ideas, based on their experience 

as well as their research, to assist in 

prioritizing elements and development 

of the AI RMF. Stakeholders include but 

are not limited to industry, civil society 

groups, academic institutions, federal 

agencies, state, local, territorial, tribal, 

and foreign governments, standards 

developing organizations and 

researchers. The Framework is intended 

to address AI risk management related 

to individuals, groups or organizations 

involved in the design, development, 

use, and evaluation of AI systems. 

The goals of the Framework 

development process, generally, and 

this RFI, specifically, are to: 

1. Identify and better understand 

common challenges in the design, 

development, use, and evaluation of AI 

systems that might be addressed 

through a voluntary Framework; 

2. gain a greater awareness about the 

extent to which organizations are 

identifying, assessing, prioritizing, 

responding to, and communicating AI 

risk or have incorporated AI risk 

management standards, guidelines, and 

best practices, into their policies and 

practices; and 

3. specify high-priority gaps for which 

guidelines, best practices, and new or 

revised standards are needed and could 

be addressed by the AI RMF—or which 

would require further understanding, 

research, and development. 

Details About Responses to This 

Request for Information 

When addressing the topics below, 

respondents may describe the practices 

of their organization or organizations 

with which they are familiar. They also 

may provide information about the type, 

size, and location of those 

organization(s) if they desire. Providing 

such information is optional and will 

not affect NIST’s full consideration of 

the comment. Respondents are 

encouraged to provide generalized 

information based on research and 

potential practices as well as on current 

approaches and activities. 

Comments containing references, 

studies, research, and other empirical 

data that are not widely published (e.g., 

available on the internet) should 

include copies of the referenced 

materials. All submissions, including 

attachments and other supporting 

materials, will become part of the public 

record and subject to public disclosure. 

NIST reserves the right to publish 

relevant comments publicly, unedited 

and in their entirety. All relevant 

comments received by the deadline will 

be made publicly available at https://

www.nist.gov/itl/ai-risk-managementframework and at regulations.gov. 

Respondents are strongly encouraged to 

use the template available at: https://

www.nist.gov/itl/ai-risk-managementframework. 

Personally identifiable information 

(PII), such as street addresses, phone 

numbers, account numbers or Social 

Security numbers, or names of other 

individuals, should not be included. 

NIST asks commenters to avoid 

including PII as NIST has no plans to 

redact PII from comments. Do not 

submit confidential business 

information, or otherwise sensitive or 

protected information. Comments that 

contain profanity, vulgarity, threats, or 

other inappropriate language or content 

will not be considered. NIST requests 

that commenters, to the best of their 

ability, only submit attachments that are 

accessible to people who rely upon 

assistive technology. A good resource 

for document accessibility can be found 

at: section508.gov/create/documents. 

Specific Requests for Information 

The following statements are not 

intended to limit the topics that may be 

addressed. Responses may include any 

topic believed to have implications for 

the development of an AI RMF, 

regardless of whether the topic is 

included in this document. All relevant 

responses that comply with the 

requirements listed in the DATES and 

ADDRESSES sections of this RFI and set 

forth below will be considered. 

NIST is requesting information related 

to the following topics: 

1. The greatest challenges in 

improving how AI actors manage AIrelated risks—where ‘‘manage’’ means 

identify, assess, prioritize, respond to, 

or communicate those risks; 

2. How organizations currently define 

and manage characteristics of AI 

trustworthiness and whether there are 

important characteristics which should 

be considered in the Framework 

besides: Accuracy, explainability and 

interpretability, reliability, privacy, 

robustness, safety, security (resilience), 

and mitigation of harmful bias, or 

harmful outcomes from misuse of the 

AI; 

3. How organizations currently define 

and manage principles of AI 

trustworthiness and whether there are 

important principles which should be 

considered in the Framework besides: 

Transparency, fairness, and 

accountability; 

4. The extent to which AI risks are 

incorporated into different 

organizations’ overarching enterprise 

risk management—including, but not 

limited to, the management of risks 

related to cybersecurity, privacy, and 

safety; 

5. Standards, frameworks, models, 

methodologies, tools, guidelines and 

best practices, and principles to 

identify, assess, prioritize, mitigate, or 

communicate AI risk and whether any 

currently meet the minimum attributes 

described above; 

6. How current regulatory or 

regulatory reporting requirements (e.g., 

local, state, national, international) 

relate to the use of AI standards, 

frameworks, models, methodologies, 

tools, guidelines and best practices, and 

principles; 

7. AI risk management standards, 

frameworks, models, methodologies, 

tools, guidelines and best practices, 

principles, and practices which NIST 

should consider to ensure that the AI 

RMF aligns with and supports other 

efforts; 

8. How organizations take into 

account benefits and issues related to 

inclusiveness in AI design, 

development, use and evaluation—and 

how AI design and development may be 

carried out in a way that reduces or 

manages the risk of potential negative 

impact on individuals, groups, and 

society. 


jbell on DSKJLSW7X2PROD with NOTICES
Federal Register / Vol. 86, No. 143 / Thursday, July 29, 2021 / Notices 40813 

1U.S., House, Committee on Armed Services, 

National Defense Authorization Act for Fiscal Year 

2019 (H. Rpt. 115–676). Washington: Government 

Printing Office, 2018. 

2MAJOR Tierney, Raymond G., The Federal 

Warfare Systems Laboratory Executive Summary, 

Available at: https://www.nist.gov/system/files/ 

documents/2021/05/27/FWS%20LAB_

2021%20White%20Paper_v17.2021APR19.pdf. 

Accessed: 7/13/2021. 

3Excerpt from the 2018 National Defense 

Strategy. 

9. The appropriateness of the 

attributes NIST has developed for the AI 

Risk Management Framework. (See 

above, ‘‘AI RMF Development and 

Attributes’’); 

10. Effective ways to structure the 

Framework to achieve the desired goals, 

including, but not limited to, integrating 

AI risk management processes with 

organizational processes for developing 

products and services for better 

outcomes in terms of trustworthiness 

and management of AI risks. 

Respondents are asked to identify any 

current models which would be 

effective. These could include—but are 

not limited to—the NIST Cybersecurity 

Framework or Privacy Framework, 

which focus on outcomes, functions, 

categories and subcategories and also 

offer options for developing profiles 

reflecting current and desired 

approaches as well as tiers to describe 

degree of framework implementation; 

and 

11. How the Framework could be 

developed to advance the recruitment, 

hiring, development, and retention of a 

knowledgeable and skilled workforce 

necessary to perform AI-related 

functions within organizations. 

12. The extent to which the 

Framework should include governance 

issues, including but not limited to 

make up of design and development 

teams, monitoring and evaluation, and 

grievance and redress. 

Authority: 15 U.S.C. 272(b), (c), & (e); 

15 U.S.C. 278g–3. 

Alicia Chambers, 

NIST Executive Secretariat. 

[FR Doc. 2021–16176 Filed 7–28–21; 8:45 am] 

BILLING CODE 3510–13–P 

DEPARTMENT OF COMMERCE 

National Institute of Standards and 

Technology 

Establishment of a Laboratory 

Accreditation Program for 

Laboratories Performing System 

Integration Testing and Operational/ 

User Acceptance Testing on Federal 

Warfare Systems Under the National 

Voluntary Laboratory Accreditation 

Program 

AGENCY: National Institute of Standards 

and Technology, Commerce. 

ACTION: Notice. 

SUMMARY: Under the National Voluntary 

Laboratory Accreditation Program 

(NVLAP) the National Institute of 

Standards and Technology (NIST) 

announces the establishment of a 

laboratory accreditation program and 

the availability of applications for 

accreditation of laboratories that 

perform System Integration Testing 

(SIT) and Operational/User Acceptance 

Testing (O/UAT) on Federal Warfare 

Systems. 

ADDRESSES: Laboratories may obtain 

NIST Handbook 150, NVLAP 

Procedures and General Requirements, 

NIST Handbook 150–872, Federal 

Warfare System(s), and an application 

for this program by visiting the NVLAP 

website at https://www.nist.gov/nvlap or 

by sending a request to NVLAP by mail 

at NIST/NVLAP, 100 Bureau Drive, Stop 

2140, Gaithersburg, MD 20899–2140 or 

by email at nvlap@nist.gov. All 

applications for accreditation must be 

submitted to nvlap@nist.gov. 

FOR FURTHER INFORMATION CONTACT: Brad 

Moore, Program Manager, NIST/NVLAP, 

100 Bureau Drive, Stop 2140, 

Gaithersburg, MD 20899–2140, Phone: 

(301) 975–5740 or email: 

bradley.moore@nist.gov. 

Information regarding NVLAP and the 

accreditation process can be obtained 

from https://www.nist.gov/nvlap. 

SUPPLEMENTARY INFORMATION: In 

response to the need for an improved 

capability to protype and experiment 

prior to generating requirements, the 

U–2 Federal Laboratory was established 

in accordance with 15 U.S.C. 3710 and 

10 U.S.C. § 2500. The U–2 Federal 

Laboratory’s mission is to ‘‘[f]ast-field 

advanced technologies at a speed 

relevant to the warfighter,’’ in 

accordance with House Report 115–676 

(2018) 1 and the Congressionallymandated 2018 National Defense 

Strategy. This is accomplished through 

vertical integration with one laboratory 

to effect ‘‘[c]onfluence of Warfighter, 

Developer, and Acquirer.’’ 2

On May 7, 2019, the U–2 Federal 

Laboratory formally requested in writing 

the Chief of NVLAP consider the 

establishment of a proposed new 

Laboratory Accreditation Program (LAP) 

entitled, ‘‘Federal Warfare System(s) 

LAP,’’ in accordance with NIST 

Handbook 150 Para 2.1.3. In compliance 

with NVLAP procedures (15 CFR part 

285), NVLAP held a public workshop on 

November 19, 2019 to solicit further 

comments on the establishment of a 

Federal Warfare System(s) LAP and on 

the technical requirements to be 

associated with the LAP. 

Determination 

Under the framework of the Federal 

Warfare Systems Laboratory, advanced 

technologies can be developed or 

integrated to determine technical 

feasibility (‘‘Is it possible?’’). Embedded 

developers then hand the technology to 

the end-user (‘‘Warfighter’’) to 

determine operational utility (‘‘Is it 

useful?’’). This process continuously 

cycles between development and 

operations. The desired outcome is 

achieved when the technology has 

evolved to a high-Technology Readiness 

Level (TRL), Warfighter-useful solution. 

At this point, the technology generally 

transitions into the Joint Capabilities 

Integration and Development System 

and Defense Acquisition System (DoD 

Directive 5000.01 and DoD Instruction 

5000.02) as a vetted, mature 

requirement. In this way, the 

acquisitions process is meaningfully 

compressed, and cost offsets realized, by 

(a) front-loading development with the 

end-user and (b) abating the problems of 

scope, understanding, and volatility 

associated with the requirement 

development process. Importantly, 

establishment of this LAP affords a 

means to standardize the traceability, 

competence, impartiality, and 

operational consistency of Federal 

Laboratories supporting warfare systems 

within the Department of Defense, as 

well as a means to meet a 2018 National 

Defense Strategy mandate that, 

‘‘prototyping and experimentation 

should be used prior to defining 

requirements.’’ 3

The U.S. Air Force Air Combat 

Command (ACC) Office of the Chief 

Scientist is considering a commandwide plan for adoption of the Federal 

Warfare System Laboratory construct. 

Interest in this concept has also been 

expressed by senior military leaders. 

Based on careful analysis of 

comments received during the public 

workshop and a review of the Secretary 

of Defense’s strategies, instructions, and 

mandates, the Chief of NVLAP has 

determined that the establishment of a 

LAP for laboratories conducting SIT and 

O/UAT on Federal Warfare Systems best 

meets government needs. 

This notice is issued in accordance 

with NVLAP procedures and general 

requirements, found in 15 CFR part 285. 

NVLAP provides an unbiased, thirdparty evaluation and recognition of 

competence. NVLAP accreditation 

signifies that a laboratory has 


jbell on DSKJLSW7X2PROD with NOTICES