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RFI Response

September 15, 2021

Virginia Tech Applied Research Corporation (VT-ARC)

In Partnership with

Virginia Polytechnic Institute and University (VT) & 

Commonwealth Cyber Initiative (CCI)

(Comments included from R. Kuhn, R. Kacker, M.S. Raunak, NIST)

Contract Name: Artificial Intelligence Risk Management Framework

Notice ID: NIST-2021-0004; Docket Number: 210726-0151

Technical points of contact

Name – Laura Freeman; Feras A. Batarseh

Address - 900 N Glebe Rd, Arlington, VA 22203

Telephone Number - (571) 384-3833

Facsimile Number - (571) 449-3713

E-mail Address – laura.freeman@vt.edu; batarseh@vt.edu

VT-ARC Organizational DUNS: 078432677

VT Organizational DUNS: 00-313-7015

Disclosure of Information Statement:

This white paper includes data that shall not be disclosed outside the Government, except to non-Government 

personnel for evaluation purposes, and shall not be duplicated, used, or disclosed -- in whole or in part -- for any 

purpose other than to evaluate this submission. If, however, an agreement is issued to this Company as a result of --

or in connection with – the submission of this data, the Government shall have the right to duplicate, use, or disclose 

the data to the extent agreed upon by both parties in the resulting agreement. This restriction does not limit the 

Government's right to use information contained in this data if it is obtained from another source without restriction. 

The data subject to this restriction are contained in ALL sheets contained within.
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A. Statement of Interest

Both VT-ARC and VT are very interested in supporting the National Institute of Standards and 

Technology (NIST) with development of a framework that can be used to improve the 

management of risks to individuals, organizations, and society associated with artificial 

intelligence (AI). VT-ARC and VT are integrated in their efforts supporting many different 

Artificial Intelligence (AI) and Machine Learning (ML) programs and provide the most cuttingedge technical advances in the AI/ML area.

Virginia Tech Applied Research Corporation (VT-ARC). VT-ARC is a private non-profit 

501(c)(3), applied research corporation affiliated with Virginia Polytechnic Institute and State 

University (Virginia Tech or VT). VT-ARC accelerates solutions to complex issues of national 

importance through the identification, development and application of innovative analytics and 

advanced technologies. We leverage the rich, multidisciplinary research and innovation ecosystem 

of Virginia Tech, combining strategy, policy, technology, and operational considerations across 

multiple domains. VT-ARC has past performance over the past 10 years with multiple federal 

government organizations, industry partners, DOD innovation organizations, and other higher 

education and research institutions. VT-ARC has performed on two separate PIAs, one with ARL 

for nearly five years, and the other with AFRL/AFOSR for seven years. Among our partners are 

the ARL, the ARO, AFOSR, OUSDR&E, and DTRA. We currently support 16 research, 

development, planning and testing programs.

VT-ARC’s headquarters, located in Arlington, VA, has over 16,000 total square feet of space, 

including 13,000 square feet of unclassified office space, additional TS secure classified spaces, 

and building access to venues that can support events up to 200 people. Our second office is located 

in Blacksburg, VA. While VT-ARC doesn’t possess designated laboratory space, we can access 

university laboratory space through Virginia Tech.

VT Hume Center Intelligent Systems Lab (ISL). The Virginia Tech Hume Center believes 

providing research opportunities to students is key for developing the next generation of national 

security and technology leaders. Our research opportunities allow students to gain hands-on 

experience in their preferred discipline, network and build connections with award-winning and 

expert-level researchers in the field and explore an academic discipline more fully to develop 

interests into passions and careers.

The lines between data science, machine learning, and cybersecurity are blurring as algorithms 

that include machine learning are integrated into production systems. The validation of complex 

systems through rigorous test and evaluation processes is also needed to ensure the efficacy and 

safety of algorithms embedded in systems as they accomplish tasks with greater autonomy and 

operational impact. The design and development of this systems need to reflect their intended 

operating environment, representative human users, and the operational mission/tasks. The Hume 

Center's ISL conducts research to address critical areas of national security in three technological 

thrusts: 1) data science, machine learning, artificial intelligence, 2) cybersecurity and complex 

systems engineering, and 3) complex system design, validation, and test and evaluation (T&E).

Commonwealth Cyber Initiative (CCI). VT has a leadership role in developing AI assurance 

methods, and in managing the CCI AI Testbed. CCI supports over 70 participants from over 30 

different Virginia universities and colleges. The AI assurance team at CCI develops model 
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agnostic and model specific assurance pipelines for a variety of applications including for public 

policy, government systems, American critical infrastructure (such as smart grids and smart 

farms), and other domains. The infrastructure within the AI testbed enables the widescale testing 

of such deployments, including clusters designed to allocate logically separated work 

environments. containers that enable users to run their data science, machine learning (ML), and/or 

AI workloads. This construct enables different modes of operation and the ingest of relevant data 

to support threat detection, identification, framework alignment, correlation, and analytics 

workflows. These workflows can be staged on the testbed where various types of data science 

techniques can be applied. The research and development main focus of the AI assurance team at 

CCI is the assurance and risk mitigation of AI systems within different domains and sectors of 

society. The team is currently spearheading a publication of a book (to be released on March, 2022) 

on the assurance of AI systems and methods for AI risk management. This team is very excited to 

support this request from the NIST.

NIST REQUESTED INFORMATION

VT and VT-ARC have reviewed the requested NIST RFI and AI Framework. Two of our PhDs in

the VT Hume Intelligence Systems Lab, Dr. Laura Freeman and Dr. Feras A. Batarseh are well 

published in areas paralleling your AI Framework effort. Many of the NIST AI Framework 

characteristics are specifically called out and explained in select recent manuscripts. The first 

paper: a survey on AI assurance [1] provides a roadmap to challenges that need to be resolved to 

create effective risk management frameworks. The second paper: enabling AI adoption through 

assurance [2], proposes a generic framework that addresses AI risk challenges. The third paper 

provides metrics for testing machine learning algorithms [3]. Test and Evaluation for AI [4] argues 

that a test and evaluation process where metrics as discussed in [3] are captured is key to a riskbased framework for supporting the decision to deploy AI into defense systems. The final reference 

provides a first pass at critical constructs to be developed I a framework for assuring autonomous 

intelligent agents. Incorporating some of the terminology and framework characteristics should be 

beneficial to your final proposal. Our team has taken excerpts from these documents and 

commented in the appropriate sections of the RFI. The five documents are referenced up front and 

attached in this .pdf response.

We look forward to supporting you on this effort!

1. Batarseh, F., Freeman, L. and Huang, C., “A Survey on Artificial Intelligence Assurance”, 

Journal of Big Data 8, 60, Apr 2021.

2. Freeman, L., Rahman, A., Batarseh, F., “Enabling Artificial Intelligence Adoption Through 

Assurance”, accepted at MDPI’s Journal of Social Sciences, Artificial Intelligence for Policy 

Analysis, Governance and Society (AI-PAGES), Sep 2021.

3. Lanus, E., Freeman, L.., Kuhn, R. and Kacker, R. "Combinatorial Testing Metrics for Machine 

Learning," 2021 IEEE International Conference on Software Testing, Verification and 

Validation Workshops (ICSTW), 2021, pp. 81-84, doi: 10.1109/ICSTW52544.2021.00025.

4. Freeman, L. (2020). Test and Evaluation for Artificial Intelligence. INSIGHT, 23(1), 27-30.

5. Lanus, Erin, et al. "Test and Evaluation Framework for Multi-Agent Systems of Autonomous 

Intelligent Agents." arXiv preprint arXiv:2101.10430 (2021).
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NIST is requesting information related to the following topics:

1. The greatest challenges in improving how AI actors manage AI related risks—where 

‘‘manage’’ means identify, assess, prioritize, respond to, or communicate those risks

VT/VT-ARC Comments: Another way of stating how an agency can manage AI risks is 

through evaluation of the AI system stated as assurance, validation, verification. [1]. The areas 

of focus for management and organization of AI are domain, AI subarea and the AI goal. [1]

An overall definition of AI assurance would add significantly to the user’s focus when 

analyzing what NIST is trying to accomplish and how NIST is going to manage AI risk. Based

on prior definitions and recent AI challenges, we propose the following definition for AI

assurance:

A process that is applied at all stages of the AI engineering lifecycle ensuring that any 

intelligent system is producing outcomes that are valid, verified, data-driven, trustworthy, and 

explainable to a layman, ethical in the context of its deployment, unbiased in its learning, and 

fair to its users. [1]

Areas to assist with management of AI and essential to all forms of AI Assurance

(1) Context: refers to the scope of the system, which could be associated with a timeframe, 

a geographical area, specific set of users, and any other system environmental 

specifications 

(2) Correlation: the amount of relevance between the variables, this is usually part of 

exploratory analysis, however, it is key to understand which dependent variables are 

correlated and which ones are not

(3) Causation: the study of cause and effect, i.e., which variables directly cause the outcome 

to change (increase or decrease) in any fashion

(4) Distribution: whether a normal distribution is assumed or not. Data distribution of the 

inputted dependent variables can dictate which models are best suited for the problem at 

hand

(5) Attribution: aims at allocating the variables in the dataset that have the strongest 

influence on the outcomes of the AI algorithm. [1]

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.

VT/VT-ARC Comments. An alternate AI Assurance definition is the probability that a system 

leveraging AI algorithms functions only as intended and is free of vulnerabilities throughout 

the life cycle of the system. High levels of assurance stem from all the planned and systematic 

activities applied at all stages of the AI engineering lifecycle ensuring that any intelligent 

system is producing outcomes that are valid, verified, data-driven, trustworthy, and explainable 

to a layman, ethical in the context of its deployment, unbiased in its learning, and fair to its 
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stakeholders. The probability that a system functions as intended includes assessments across 

constructs to include trustworthiness, explainability, fairness, assessment of unintended biases, 

and the assessment of ethical implications. [2]. Our recommended verbiage for this section 

is inclusion of the terms fairness, and ethical implications.

The areas of focus for management and organization of AI are domain, AI subarea and the AI 

goal. [1]

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.

VT/VT-ARC Comments. In order to add considerations to this section, AI trustworthiness 

should first be defined. Trustworthiness includes concepts such as integrity, human-centered 

development and use, respect the law, convey that humans must be able to trust the AI 

algorithms for wide-scale (agency level) adoption. [2]

Additional principles contributing to AI trustworthiness include reliability, robustness, 

security, and ethical implications.

Each of these terms is defined below for consideration.

Reliability, Robustness - highlight the need to understand the consistency of algorithm 

prediction across varying domains and understand when they may fail

Secure - Algorithms and data should be protected and controlled at the appropriate

level, algorithms need to be robust to adversarial action

Ethical - as AI scales solutions, it also scales mistakes, discrimination, and potential nonethical outcomes. Algorithms ought to be assured to ensure ethical standards (within the 

context it is applied) are met [2]

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.

VT/VT-ARC Comments. Our team recommends adding the language - Algorithms and data 

should be protected and controlled at the appropriate level and algorithms need to be robust 

to adversarial action. [2]

In order to apply appropriate security protocols, our team recommends the following approach 

as stated in the RMF framework: Categorize the Information System, Select Security Controls, 

Implement Security Controls, Assess Security Controls, Authorize Information System, 

Monitor Security Controls

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
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VT/VT-ARC Comments. AI measurement and risk assessment should stem from the problem 

definition. Some overall measurement areas are algorithm performance, bias/fairness, security, 

safety, trustworthiness, explainability, ethicality. Additionally, traditional measurements of 

systems should be considered to include system performance, utility, efficacy, usability, 

resilience, reliability, and maintainability. [2]

It is essential to evaluate the AI system’s ability to respond correctly to inputs. One aspect of 

this is ensuring that the input model used for training and testing AI systems accurately reflects 

the range of inputs that will be encountered in use. We have developed methods for measuring 

input space coverage that can be applied to the problem of quantification in measurement areas 

of security, safety, and explainability [3]. 

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.

Today, the strongest regulatory requirements for software and systems safety are those used in 

commercial aviation, based on recommendations from the Radio Technical Commission for 

Aeronautics (RTCA), primarily RTCA DO-178C. The key method for testing life-critical 

software in aviation, as required in DO-178C, is the modified condition/decision coverage 

(MCDC) criterion, which requires that every decision within code takes every possible 

outcome, each condition within each decision takes every possible outcome, and every 

condition in every decision has been shown to independently affect the decision outcome.

The MCDC criterion is extraordinarily time-consuming and expensive to achieve, typically 

consuming 85% to 90% or more of the software development budget (NASA study). For 

autonomous systems, there is an even more significant assurance problem: large parts of these 

systems use neural network or other black-box software that is difficult if not impossible to 

verify and test according to the MCDC criterion, or other structural coverage measures such 

as branch or statement coverage. The reason is that the behavior of neural networks depends 

on connections formed based on large volumes of input, and not on hard-coded logic and 

decision predicates as in conventional software. Testing and assurance of autonomous systems 

including neural nets is an unsolved problem and the subject of much current 

research. Methods of addressing assurance in this space have focused on measuring neuron 

coverage, but it is far from clear if neuron coverage has a significant relationship with 

correctness and safety in autonomous systems. It may be that neuron coverage is too simple 

of a metric that is not sufficiently indicative of system behavior, just as statement coverage in 

conventional software is a poor measure of test thoroughness or correctness. Most efforts at 

assuring correct operation of autonomous systems have been based on testing for extended 

periods, with inadequate measures of how thorough such testing has been other than time, 

number of miles driven, or other basic quantities. Such brute force testing may be insufficient 

for preventing failures under extremely rare circumstances. A well-known example is the fatal 

crash of a car in autonomous mode that resulted from a very rare four-factor combination of a 

white truck against a brightly lit sky, along with truck height and angle versus the car [1,2].

Measurements of input space model coverage are one approach to addressing the problem of 
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ensuring that inputs to the AI system are processed correctly, and these measures could be 

considered for future standards. 

The IEEE Reliability Society is currently in the process of defining standards for autonomous 

systems. Notable with respect to trust and assurance in particular are IEEE 7001 and 7009, 

which address AI explainability and safety. These developing standards address a number of 

areas that are included in the proposed risk management framework.

• IEEE 7001 - Transparency of Autonomous Systems

– http://standards.ieee.org/develop/project/7001.html

• IEEE 7009 - Fail-Safe Design - Standard for Fail-Safe Design of Autonomous and SemiAutonomous Systems

– https://standards.ieee.org/project/7009.html

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

VT/VT-ARC Comments. The language ‘ensure that the AI RMF aligns with and supports 

other efforts’ is key to AI risk management. In order to adequately align with other efforts, 

the ‘Define’ phase of the process ensures all stakeholders are involved in the process.

Stakeholders may include the mission/task champion (leadership), program management, 

system engineer, AI developer, requirements representative, test and evaluation personnel, end 

users, etc. depending on the application. The three most important elements in the ‘Define’ 

phase are the AI subarea, AI Domain, and the AI goals. Critical elements of this phase are:

AI Domain - what is the broad organizational mission that is acquiring the AI enabled

system (e.g., government, energy sector, defense, healthcare, etc.)? What context does that 

bring to the challenge of assuring AI? 

Mission context - what is the problem that the organization is trying to solve with an AI 

system(s)? How does the AI support the organizational mission? 

AI Subarea - what is the type of AI needed (e.g., deep learning, reinforcement learning, 

etc.)? How does the AI contribute in ways that previous methods failed?

Scientific/Engineering Alignment - What are the scientific and/or engineering needs that 

the AI can solve? How does it integrate with know constraints? How does it incorporate 

prior knowledge (solely through data or other mechanisms)?

AI Goal -What are the primary directives for the AI, how does this stem from the AI 

domain? Must it be ethical, explainable, fair, etc.? [2]

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.

VT/VT-ARC Comments. Defining testing and assurance are critical to this section. Defining 

both of those here would be very helpful to the overall framework. We provide definitions 

applicable to all AI domains and subareas.
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Testing: according to the American Software testing Qualification Board, testing is “the 

process consisting of all lifecycle activities, both static and dynamic, concerned with planning, 

preparation and evaluation of software products and related work products to determine that 

they satisfy specified requirements, to demonstrate that they are fit for purpose and to detect 

defects”. Based on that (and other reviewed definitions), testing includes both verification and 

validation. [2]

Verification is the “process of determining that a model implementation accurately 

represents the developer’s conceptual descriptions and specifications” [2]

Validation is the process of “determining the degree to which a model is an accurate 

representation.” [2]

Assurance: this term has been rarely applied to conventional software engineering; rather, it is 

used in the context of AI and learning algorithms. Our definition of AI assurance in included 

in our comments to section 1 of this document. Based on prior definitions and recent AI 

challenges, we propose the following definition for AI assurance:

A process that is applied at all stages of the AI engineering lifecycle ensuring that any 

intelligent system is producing outcomes that are valid, verified, data-driven, trustworthy 

and explainable to a layman, ethical in the context of its deployment, unbiased in its 

learning, and fair to its stakeholders. [1]

9. The appropriateness of the attributes NIST has developed for the AI Risk Management 

Framework. (See above, ‘‘AI RMF Development and Attributes’’)

• 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

• 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.

• 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.

• 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
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should be platform- and technology agnostic and customizable. It should meet the 

needs of AI designers, developers, users, and evaluators alike.

• 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 better manage AI risks for their intended use 

cases or scenarios.

• Be readily usable as part of any enterprise’s broader risk management strategy and 

processes.

• 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.

• 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.

VT/VT-ARC Comments. Our team concurs with the RMF Framework as presented and offers 

language for consideration to add additional context for all of the sections in this framework. 

This RMF framework meets our characteristics for AI assurance as reliable, dependable, 

explainable, and fair. AI assurance provides the necessary tools to enable AI adoption into 

applications, software, hardware, and complex systems. AI assurance involves quantifying 

capabilities and associating risks across deployments including: data quality to include inherent 

biases, algorithm performance, statistical errors, and algorithm trustworthiness and security. 

Our recommendation acknowledges that data, algorithmic, and context/domain-specific 

factors may change over time and impact the ability of AI systems in delivering accurate 

outcomes. [1]

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
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Recommend adding this process from the International Statistical Engineering Association 

(Figure 1)

Our overall process for AI assurance is six steps--Define, Measure, Characterize & Add 

Context, Plan Strategy, Execute & Analyze, and Monitor & Improve. [2]

Define. The define stage sets up the foundation for the AI assurance process. This phase should 

focus on ensuring that the AI assurance program is adequate. A key aspect of the define phases 

is ensuring that all stakeholders are involved in the defining process. Stakeholders may include 

the mission/task champion (leadership), program management, system engineer, AI developer, 

requirements representative, test and evaluation personnel, end users, etc. depending on the 

application. Based on an extensive literature review Batarseh, Freeman, and Huang [10] 

identify three elements of AI assurance that are important in the “Define” phase: AI Subarea, 

AI Domain, and AI Goals. [2]

Measure. There are numerous dimensions of AI measurement and not all intelligent systems 

will require all dimensions. The measures should stem from the problem definition. 

Dimensions of measurements that should be considered include: Algorithm Performance, 

Bias/Fairness, Security, Safety, Trustworthiness, Explainability, Ethicality. Although those 

measures are highly subjective, methods to quantify them are needed to further advancing the 

field. The loading of ethical standards or trustworthiness for instance into an AI system is -in 

most cases- domain specific; for instance, an ethical metric in a system built for a healthcare 

application will be -most likely- different than one in a warfare application, therefore, the 

process of "values loading" into the AI system is needed to capture those measures and create 

a benchmark to compare against for assurance quantification. [2]

Characterize & Add Context. The measurement of intelligent systems leveraging AI is 

dependent on the multi-dimensional space that includes the operational environment, system, 

hardware infrastructure, and other factors effecting the AI. The concept of an operating 

envelope applies at each view level. For example at the operational environment view one 

might be concerned about factors that include environmental conditions, operational users,

mission tasks, and other factors external to the system impact performance outcomes. At the 

lower level the operating envelope many consist of data views like data quality, information

content, domain, and range. Each of these views needs to be considered for their impact on 

achieving the goals of the intelligent system and appropriately planned for in the execution of 

the assurance program. [2]

Figure 1. Typical Statistical Engineering Process, adapted from Hoerl and Snee (2017)[2]
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Plan Strategy. We emphasize the need for leveraging test and evaluation across the system 

development, deployment, and operations life-cycle as the basis for a strategy for assuring AI.

Our research highlights the need for an iterative processes and several quality improvement 

tasks that include multiple types of testing, but also debugging, manual inspection, and static 

analysis. While formal verification strategies can be a component of an assurance strategy, the 

complexity of statistical learning algorithms - especially when considered as employed as part 

of a system that interacts with the operational environment - demands a data driven approach 

to assurance. The planning strategy should consider the use of formal verification approaches 

as well as test venues that enable the exploration of AI model outputs directly, the incorporation 

of digital simulations, software-in-the-loop simulations, hardware-in-the loops simulations, 

full system testing in controlled environments, and full system testing in operational 

environments with operational users. The planning strategy should describe the iterative, 

sequential process for gathering knowledge across the key measurements for AI assurance. 

Testing the system should occur across the developmental and engineering life-cycle. 

Statistical methods such as design of experiments, sequential-adaptive test design techniques 

(e.g., response surface methods, optimal learning), and stratified random sampling for 

algorithms training, testing, should be employed to provide a systematic approach to scaling 

information collection with system development and ensuring adequate information exists 

across the various views on operating envelopes exists to support the assurance assessment.

[2]

Execute & Analyze. The execution phase of testing should also include ongoing analyses of 

the data, assessments of feasibility, and lessons learned. Analyses should consider knowledge

gaps based on planning strategies and dedicated time for reworking the test program based on 

information obtained during each phase of testing. [2] 

Monitor & Improve. Our research recommends a continuous process of T&E for autonomous 

intelligent agents that shows that this data collection process is followed throughout 

development, manufacturing, deployment, and operations. In operations, they emphasize the 

need for both ongoing monitoring of the systems capabilities, independent assessment when 

pre-defined triggering events such as a mission objective change or an adversarial attack. 

Additionally, software defined functionality in general provides the opportunity to improve 

capabilities without a full redesign of the system. AI algorithms can be improved by 

introducing new more relevant data for the current tasking. Methods such as transfer learning 

and targeted fine-tuning provide the opportunity for continual improvement to fielded 

algorithms. However, robustness is always a consideration that must be considered when 

making algorithm improvements. The monitor phase is essential to consider from the early 

phases of system design. Monitoring is an easier task when the system is designed to output 

relevant measures and metrics automatically. [2]

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.

No Comment
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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.

No Comment