Arm, Inc. 

comments to 

Draft “NISTIR 8312, Four Principles of Explainable Artificial Intelligence”

October 15, 2020

On behalf of Arm, we are pleased to have the opportunity to submit the following comments 

on the draft National Institute of Standards and Technology Interagency or Internal Report 

(“NISTIR”) 8312, Four Principles of Explainable Artificial Intelligence. More broadly Arm is 

supportive of the work NIST is performing to examine a framework for trustworthy artificial 

intelligence (“AI”), of which explainable AI is an important component. 

Arm is the leading, and largest supplier of intellectual property in the semiconductor sector. 

Arm provides central processing units, graphics processing units, and neural processing units, as 

well as a portfolio of other technologies that enable AI from the cloud to endpoint devices and 

everywhere between. In fact, the majority of consumer interactions with AI today are likely 

happening on Arm-based technology offered by Arm’s partners. As such, Arm has taken 

seriously the ethical implications posed by AI, and the need to ensure appropriate protections 

are taken to address those implications and ensure the public can trust this emerging 

technology. 

As a call to action on this in November 2019 Arm released the AI Trust Manifesto, setting out six

principles Arm believes companies must address to demonstrate and establish public trust for 

AI.

1

 As such, Arm supports efforts by NIST to address components of trustworthy AI, and 

believes the ability to explain AI outputs is essential to trust.

2

 Arm included explainability as 

one of the six principles in Arm’s AI Trust Manifesto, explicitly stating: 

3/ WE BELIEVE AI SHOULD BE CAPABLE OF EXPLAINING ITSELF AS MUCH AS POSSIBLE: 

WE URGE FURTHER EFFORT TO DEVELOP TECHNOLOGICAL APPROACHES TO HELP AI 

SYSTEMS RECORD AND EXPLAIN THEIR RESULTS Where appropriate, the way an AI 

system works should be capable of being transparent, and the decisions that result from 

it should be explainable to a human interrogator including non-specialist users of AI.3

Comments on NISTIR 8312

In general, Arm agrees with the ideas expressed in the Introduction. Explainability is one of 

several properties that will build public trust in AI systems.

4

 Similar to NISTIR 8312, the Arm AI 

Trust Manifesto also expresses the need to address accountability and bias. The aim of Arm’s 

inclusion of “security” as a key principle in the Arm AI Trust Manifesto, contributes to two other 

1 See Arm AI Trust Manifesto https://www.arm.com/blogs/blueprint/wp-content/uploads/2019/11/Arm-AI-TrustManifesto-2019.pdf

2 NISTIR 8312 suggests this as well, lines 128-132.

3 Arm AI Trust Manifesto.

4 NISTIR 8312, lines 133-135. 
properties included in NISTIR 8312, “resiliency” and “reliability” which are dependent on 

security.5

2. For Principles of Explainable AI

The four principles of explainable AI set forth in the paper are each well defined, and important 

components of building trust in AI.6 In particular, the discussion throughout this section of the 

different needs for different user groups is very important, as is the insinuation that flexibility

may be needed under each principle to make an explanation relevant.

The discussion of the “meaningful” principle under 2.2 is important, particularly that an 

explanation is not one size fits all.7 This flexibility is important given the wide variety of 

applications in which AI will likely be used, and the wide range of technical expertise among 

users. The ability of different user groups to be able to meaningful understand or need 

different types of explanations makes sense, as does the discussion about the idea that user 

groups abilities and understanding may evolve over time and explanations that were sufficient 

for a user group in the past may no longer be sufficient.8 

Additionally, the discussion of “explanation accuracy” principle in 2.3, also provides important 

perspective on the need for flexibility in explanation accuracy metrics. A 100% accurate 

accounting of an output decision process may be too detailed or technical for a user to 

understand, and that to achieve the “meaningful” principle the accuracy metrics may need to 

be adjusted.9

The discussion of “knowledge limits” in 2.4 is an important component of establishing 

trustworthy AI. A system that provides an output simply because it is queried, but without a 

high level of confidence would certainly diminish trust. That said, based on the discussion in 

the paper, a “knowledge limit” seems like a distinct capability of an AI system rather than a 

principle of explainability. 

While discussion of different types of explanations and audiences is important, it is also

important to note that providing an explanation for every AI output is likely unworkable; during 

parts of this paper, it could be read to suggest not just an explanation for every output is 

needed, but different kinds of explanations for every output depending on the intended 

audience. The processing power needed to achieve either of these scenarios would likely be 

significant, if not insurmountable. The paper should discuss in greater detail when explanations 

for outputs are necessary. For example, are explanations necessary when there is a low level of 

risk from the outputs or tasks being performed? It should also be noted that significant work 

still needs to be done on the technology side to be able to achieve this. As focus moves from 

5 NISTIR 8312, line 134.

6 NISTIR 8312, line 151.

7 NISTIR 8312, lines 184-185.

8 NISTIR 8312, lines 198-199.

9 NISTIR 8312, lines 218-227.
principles to frameworks, flexibility will be essential as the capabilities of today’s technology are 

unlikely to be able to meet the ideal aim. 

3. Types of Explanations

This is an important section of the paper given the different audiences for an explanation 

discussed in the four principles. Acknowledging the list is not exhaustive, some types of 

explanations may be more easily delivered and well-received than others. For instance, 

“regulatory and compliance” and “system development” explanations could be easier to 

achieve than “user benefit” or “societal acceptance” as the first two are likely looking for very 

specific information where as the audience for the latter two is much broader and may be 

looking for different explanations even within that “type”. 

The example in “owner benefit” may also not be reflective of how the user ultimately makes a 

choice to watch a movie; the user may be satisfied with the explanation of why a 

recommendation was delivered, but not satisfied with the output or recommendation.

10

 

Conversely, a user may not care why a service is recommending specific movies, shows or music 

so long as the user is satisfied with the output. 

The last line of this section is incredibly important however, and points to the statement made 

earlier that the ability to deliver explanations is very much dependent on the capabilities of the 

system and the amount of computing power willing to be dedicated to explaining an output:

“The five categories of explanations illustrate the range and types of explanations and points to 

the need for flexibility in addressing the scope of systems that require explanations.”

11

Conclusion

Arm appreciates the opportunity to comment on this important product. Arm shares NIST’s 

interest in ensuring there are norms, standards and frameworks that establish trust in AI, and 

we look forward to continuing to engage as the Explainable AI work moves forward, as well as 

other work related to trustworthy AI. 

Respectfully Submitted,

Vince Jesaitis

Director, Government Affairs

Arm, Inc.

vince.jesaitis@arm.com

10 NISTIR 8312, lines 271-276.

11 NISTIR 8312, lines 310-312.