The following comments and proposed changes are in response to NIST’s solicitation of feedback

regarding NIST’s “AI Risk Management Framework: Second Draft” published August 18, 2022. Each

proposed change is accompanied by a corresponding comment to give NIST insight on why the proposed

change was made. We appreciate to the opportunity to submit the following comments.

Location

Section 1.1

(page 1)

Comment

It is important to highlight that with proper

controls AI systems can have positive

impacts.

Proposed Change

Replace the second paragraph in Section 1.1 with:

Managing AI risk is not unlike managing risk for other types

of technology. Risks to any software or information-based

system apply to AI, including concerns related to

cybersecurity, privacy, safety, and infrastructure. Like those

areas, effects from AI systems can be characterized as longor short-term, high- or low-probability, systemic or localized,

and high- or low-impact. However, AI systems bring a set of

risks that require specific consideration and approaches.

Without proper controls, AI systems can amplify,

perpetuate, or exacerbate inequitable outcomes. With

proper controls, AI systems can be used to fix inequitable

outcomes. AI systems may exhibit emergent properties or

lead to unintended consequences for individuals and

communities. A useful mathematical representation of the

data interactions that drive the AI system’s behavior is not

fully known, which makes current methods for measuring

risks and navigating the risk-benefits tradeoff inadequate. AI

risks may arise from the data used to train the AI system, the

AI system itself, the use of the AI system, or interaction of

people with the AI system.

Section 1.1 The list should be not exclusive. While the Replace the third paragraph in Section 1.1 with:

(page 1) listed characteristics are important there may

be other characteristics depending on the AI

system. For example, ISO/IEC 22989 states

that characteristics of trustworthiness

“include for instance, reliability, availability,

resilience, security, privacy, safety,

accountability, transparency, integrity,

authenticity, quality and usability.” Several

characteristics (in bold) are omitted from the

NIST Framework. To achieve harmony with

ISO/IEC 22989 and other frameworks there is

a need to for the list to be not exclusive.

It is also important to indicate that

Trustworthy AI systems include balancing

these characteristics. Balancing is called-out

in other places of this document, but it is

While views about what makes an AI technology

trustworthy differ, there are certain key characteristics

ofencompassing trustworthy systems. Trustworthy AI is valid

accuracy and reliablreliabilitye, safety, fairness and bias is

managementd, securitye and resiliencyt, accountabilityle

and transparencyt, explainabliltye and

interpretablinterpretabilitye, and privacy-enhancementd.

Creating a trustworthy AI requires balancing each of these

characteristics based the use-case of the AI system.
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Section 1.1

(pages 1-2)

important that this call-out be made early for

the readers clarity.

Societal dynamics and human

behavior/norms create the accepted

parameters for bias, fairness, interpretability,

and privacy.

Replace the fourth paragraph in Section 1.1 with:

AI systems are socio-technical in nature, meaning they are a

product of the complex human, organizational, and technical

factors involved in their design, development, and use.

Many of the trustworthy AI characteristics – such as bias,

fairness, interpretability, and privacy – are directly

Connectedinfluenced by societal dynamics and human

behavior. AI risks – and benefits – can emerge from the

interplay of technical aspects combined with socio-technical

factors related to how a system is used, its interactions with

other AI systems, who operates it, and the social context

into which it is deployed.

New Section The similarities and differences with ISO Create a new Section 1.4 entitled “Relationship to ISO

1.4 standards should be explained. This is

important because someone reading only the

NIST Framework may be unaware of the

developments in the standards world, which

NIST aims to leverage.

Standards” with the following text:

This Framework aims to harmonize efforts from other

standards organizations. Both the NIST AI Risk Management

framework and the the ISO-IEC JTC1 AI Risk Management

standard (IS:23894 currently at FDIS) are derived from the

ISO-IEC risk management framework 31000:2018. and both

projects provide recommendations towards identifying and

treating risks stemming from the use machine learning and

artificial intelligence technology. They both target AI

trustworthiness issues such as fairness, security, safety,

privacy, robustness, explainability and data quality. They

both establish a framework that incorporates organizational

aspects such as leadership, governance, design,

implementation, evaluation and continuous improvement

that is active throughout the lifecycle of AI system

development. Both projects also establish a set of processes

for risk scoping, assessment, treatment, monitoring, review,

and reporting.

The NIST AI RMF structures its framework differently, with

an objective towards being more risk based and proinnovation and highlighting AI issues in a manner that makes

the framework accessible to a broader audience. As it is

structured into a reusable core with use-case specific

profiles, it should be more appropriate and informative than

a 1 size fits all framework. Finally, as the NIST AI RMF is a

living document, it can adapt and evolve quickly to adjust to

the rapidly evolving AI/ML technology context.
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Framework and Process Recommendations Mapping

between ISO/IEC 23894 and AI RMF

Context ISO NIST

leadership and commitment 5.2 6.1 Core-Governance

Design: Understanding

context of organization 5.4.1

Assigning Org Roles 5.4.3

Scoping Context

Risk Criteria

Risk Identification

Risk Analysis

Risk Treatment

Risk Reporting

6.2 Map

6.1 Core-Governance

6.3.2- 6.2 Core Map 3

6.3.4 6.3 Core Measure

6.4.2 6.2 Core Map

6.4.3 6.2 Core Map

6.5 6.4 Core Manage

6 Core Map,

6.7 Measure, and

Manage

Section

3.2.2 (pages

9-10)

Section 3.2.2 would benefit from an explicit

call-out of low-risk and high-risk use cases.

The risk tolerance heavily depends upon if

the AI solution is a low-risk or high-risk

solution. The balancing of the key

characteristics of a trustworthy AI system will

also differ based upon the use-case of the AI

solution.

Section

3.2.3 (page

10)

The defined AI actor terms “AI development”

and “AI deployment” should be used into the

AI RMF text in order to give clarity to the

reader about the risks associated with those

roles.

Add the following paragraph before the last paragraph in

Section 3.2.2:

AI technologies can be applied to a diverse set of industries

and contexts, and consequently, a one-size-fits-all approach

is unlikely to be effective. AI systems could be considered

low-risk depending on if the AI system’s use-case poses

especially no substantial harm to people or society. The

costs of measuring reliability, robustness, resilience,

explainability, and interpretability may not be warranted in

such low-risk situations. An organization should utilize a

framework to assess the general level of risk posed by using

sector and use-case specific standards where available then

determine the appropriate level of risk management that is

warranted for the deployed AI solution.

Add the following paragraph to the bottom of Section 3.2.3:

AI actors that have different roles within an AI system can

have different risk perspectives. An AI developer who makes

AI software available, such as pre-trained models, can have a

different risk perspective than an AI deployer who

implements the AI developer’s pre-trained model in a

specific use-case. The AI deployer has the responsibility to

create a trustworthy AI system that is specific to the

deployed use-case.

Section 4 The list should be not exclusive. While the Replace the first paragraph in Section 4 with:

(page 10) listed characteristics are important there may
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

be other characteristics depending on the AI

system. There are also currently unforeseen

characteristics that will emerge with the

maturity of AI technology, so there is a need

to provide flexibility. It is also important to

indicate that Trustworthy AI systems include

balancing these characteristics. Balancing is

called-out in other places of this document,

but it is important that this call-out be made

early for the readers clarity.

Approaches which enhance AI trustworthiness can also

contribute to a reduction of AI risks. This Framework

articulates the following characteristics of trustworthy AI,

and offers guidance for addressing them. Key characteristics

of Trustworthy AI include otrustworthy systems.

Trustworthy AI is valid accuracy and reliablreliabilitye,

safety, fairness and bias is managementd, securitye and

resiliencyt, accountabilityle and transparencyt,

explainabliltye and interpretablinterpretabilitye, and

privacy-enhancementd. Creating a trustworthy AI requires

balancing each of these characteristics based the use-case of

the AI system.Trustworthy AI is: valid and reliable, safe, fair

and bias is managed, secure and resilient, accountable and

transparent, explainable and interpretable, and privacyenhanced.

Section 4

(page 11)

(1) There should be an explicit call-out that

trustworthy characteristics involve tradeoffs.

Although this call-out may appear in other

places in the document, it is important to

reiterate as it is a core concept of creating a

trustworthy AI system.

(2) The original second sentence is abstract

and unclear. These tradeoffs should be

stated in terms of their use-cases. For

example, if there is a video game AI system

that is highly secure but unfair that wouldn’t

mean that the video game AI system

necessarily be untrustworthy according to its

use-case.

(3) Organizations that deploy AI systems have

control and visibility into the risk. The AI

system cannot control risk.

Replace the text in the blue box in Section 4 with:

Trustworthiness characteristics explained in this document

are interrelated and involve tradeoffs. Highly secure but

unfair systems, accurate but opaque and uninterpretable

systems, and inaccurate but secure, privacy-enhanced, and

transparent systems are all undesirable. Trustworthy

AIOrganizations systems should consider the balanced

between achieve a high degree of control over risks

associated with AI systems and other considerations such as

while retaining a high level of performance quality, accuracy,

privacy, and explainability. Achieving this difficult goal

requires a comprehensive approach to risk management,

with tradeoffs among the trustworthiness characteristics. It

is the joint responsibility of all AI actors to determine

whether AI technology is an appropriate or necessary tool

for a given context or purpose, and how to use it

responsibly. The decision to commission or deploy an AI

system should be based on a contextual assessment of

trustworthiness characteristics and the relative risks,

impacts, costs, and benefits, and informed by a broad set of

stakeholders.

Section 4.2

(page 13)

The provided definition does not match the

definition of safety in ISO/IEC TS 5723.

Section 3.2.17 of 5723 states that “property

of a system . . . such that it does not, under

defined conditions, lead to a state in which

human life, health, property, or the

environment is endangered”

Replace the first paragraph of Section 4.2 with:

AI systems “should not, under defined conditions, cause

physical or psychological harm or lead to a state in which

human life, health, property, or the environment is

endangeredlead to a state in which human life, health,

property, or the environment is endangered” (Source:

ISO/IEC TS 5723:2022)

Section 4.3

(page 14)

The perception of fairness is highly

dependent upon an organization’s role within

the lifecycle of an AI system. This is

Replace the first paragraph in Section 4.3 with:
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

especially true when a designer or developer

develops a general purpose AI product that is

later deployed for a specific use-case. The

designer or developer may not have the

proper clarity into the deployer specific usecase to properly assess the fairness of the

deployed product. In this instance what is

fair for an AI customer may be more

accurately assessed by the deployer.

Fairness in AI includes concerns for equality and equity by

addressing issues such as bias and discrimination. Standards

of fairness can be complex and difficult to define because

perceptions of fairness differ among cultures and may shift

depending on application. Systems in which biases are

mitigated are not necessarily fair. For example, systems in

which predictions are somewhat balanced across

demographic groups may still be inaccessible to individuals

with disabilities or affected by the digital divide. Fairness

depends on an AI actor’s position in the AI system. An AI

designer AI developer for an AI product may have a different

perception of fairness as opposed to an AI deployer who

deploys the AI product.

Section 4.3 The proposed deletion section has extreme Replace the third paragraph in Section 4.3 with:

(page 14) language that has an alarmist tone. We

should be carefully balancing the potential

negative aspects of improper AI with the

benefits of properly implemented AI.

Focusing too much on the potential negative

aspects of AI is not advantageous to the

progression of AI products and services. It is

also debatable if AI can promote bias “at

speed and scale far beyond the traditional

discriminatory practices” given humans rich

history of discriminatory practices.

Bias exists in many forms, and can become ingrained in the

automated systems that help make decisions about our

lives. While bias is not always a negative phenomenon,

certain biases exhibited in AI models and systems can

perpetuate and amplify negative impacts on individuals,

groups, communities, organizations, and society – and at a

speed and scale far beyond the traditional discriminatory

practices that can result from implicit human or systemic

biases. Bias is tightly associated with the concepts of

transparency as well as fairness in society. (See NIST

Special Publication 1270, “Towards a Standard for

Identifying and Managing Bias in Artificial Intelligence.”)