Comments template for Draft SP 800-207 Please respond by November 22, 2019 Submitted by: IDSA 

Date: 11/21/19

Type: E - Editorial, G - General T - Technical 1 of 3

Submit comments by October 15, 2020 to: 

explainable-AI@nist.gov

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organization

Commenter 

name

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applicable)

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Section (if 

applicable)

Comment (Include rationale for comment) Suggested change

 1 BlackBerry 

Corporation

173-174 2.1 “The Explanation principle obligates AI systems to supply evidence, 

support, or reasoning for each output.” 

We respectfully submit our concern that the statement above is too 

constrictive to be applied to all AI systems across the board. Not all 

the AI systems are required to supply explanation. Further 

investigation is required to advance understanding of when and to 

whom explanation should be provided and for what purpose. In 

certain cases, supplying explanation could cause unwanted side 

effects, e.g., exposing vulnerabilities which could be exploited by 

adversaries. 

We propose describing the explanation principle in terms of how a 

system can satisfy the principle.

A system fulfills the explanation principle if it supplies 

evidence, support, or reasoning for each output.

 2 BlackBerry 

Corporation 

183 - 184

392 - 393

411 - 412

2.2

4

5.1

“Generally, this principle is fulfilled if a user can understand the 

explanation, and/or it is useful to complete a task” 

We generally support the intent of this principle. However, we 

propose NIST elaborate on why the term “meaningful” is chosen over 

the term “interpretable”. Sections 4 and 5.1 note that the computer 

science literature often utilizes the term “interpretable”; and that 

self-explanatory models are often labeled as “interpretable”. 

We hope NIST can make term usage more consistent if the intent is 

the same or clearly define the differences between the terms 

“interpretable” and “meaningful”. There is an explosion of research 

effort into explainable AI. To encourage progress and reduce 

confusion, a clear and concise taxonomy and terminology needs to be 

established. 

Comment Template for First Public Draft of Four 

Principles of Explainable Artificial Intelligence 

(Draft NISTIR 8312)

All comments will be made public as-is, with no edits or redactions. Please be careful to not include confidential business or personal information, otherwise sensitive or protected information, 

or any information you do not wish to be posted.
Comments template for Draft SP 800-207 Please respond by November 22, 2019 Submitted by: IDSA 

Date: 11/21/19

Type: E - Editorial, G - General T - Technical 2 of 3

 3 BlackBerry 

Corporation

211 

351 – 352

409 - 410

2.3

4

5.1

“Explanation accuracy is a distinct concept from decision accuracy.”

We generally agree with the intent of the statement above and note 

that the distinction being called out necessitates it when using the 

term “accuracy”. We think there may be some ambiguity in 

references to “accuracy” in Section 4 and 5. Examples are as shown 

below. We encourage NIST seek to help clarify the ambiguities.

“A key disagreement between philosophies is the relative importance 

of explanation meaningfulness and accuracy.”

“Although these simple models are explanations themselves, they 

are often not always accurate, ...”

4 BlackBerry 

Corporation

251-253 3 Referring to the use case of movie recommendations, we note that 

explanations are not only for the owner’s benefit but also for the 

user’s. For example, an explanation based on the user’s previous 

choices may increase his or her willingness to accept a movie 

recommendation.

User Benefit: This type of explanation is designed to inform a 

user about an output, persuade a user to accept an output or 

take a certain action. For example, the explanation could 

provide the reason for a recommendation in terms of the 

user’s previous choices, or the reason for a loan application 

denial. Explanations of this kind contribute to users’ trust.

 5 BlackBerry 

Corporation 

289 - 291 3

“From a practical perspective, explanations can be characterized by 

the amount of time the consumer of the explanation has to respond 

to the information and the level of detail in an explanation”.

We understand that the level of detail in an explanation may depend 

on the amount of time available to the consumer. However, the level 

of detail does not characterize explanation, meaningfulness (how 

helpful the explanation is for a consumer to understand it) and 

explanation accuracy (how precise the explanation reflects the real 

operation of the model). Further work is required to characterize 

explanation, meaningfulness and explanation accuracy for types or 

purpose of explanations.
Comments template for Draft SP 800-207 Please respond by November 22, 2019 Submitted by: IDSA 

Date: 11/21/19

Type: E - Editorial, G - General T - Technical 3 of 3

 6 BlackBerry 

Corporation 

267-270 3

“System development: This type of explanation assists or facilitates 

developing, improving, debugging, and maintaining of an AI 

algorithm or system”

We generally agree with the intent of the statement above. We note 

substantial research effort into explanatory debugging which 

supports debugging of leaned programs by an interactive exchange of 

explanations [ref].

We propose to add the explanatory debugging as an example of 

explanations for system deployment.

[ref] T. Kulesza et al., “Explanatory Debugging: Supporting End-User 

Debugging of Machine-Learned Programs,” in 2010 IEEE Symposium 

on Visual Languages and Human-Centric Computing, Leganes, 

Madrid, Spain, Sep. 2010, pp. 41–48, DOI: 10.1109/VLHCC.2010.15

This category includes the users requiring significant detail 

and users interacting with the system. For example, this 

may include the technical staff debugging a vision algorithm 

with a Gradient-Weighted Class Activation Mapping (GRADCAM) based tool [82] and debugging leaned programs by an 

interactive exchange of explanations [ref]. 

 7 BlackBerry 

Corporation

 724 -726 7

“To succeed in explainable AI, the community needs to study the 

interface between humans and AI systems.” 

We generally agree with the intent of the statement above. We 

know that human interaction with AI-powered systems can form an 

extended dialogue or conversation, and that such conversations can 

be understood from the view of communication and social processes 

[58]. Some treatments of explainability tacitly model explanations 

as singular outputs from a lone process, rather than results of an 

interacting agents. It may be helpful to clarify in this document that 

the social sciences may be one of the disciplines to draw upon, and 

particularly that explanations may at least sometimes take the form 

of extended and interactive explanation processes to encourage 

broad consideration of the space of explanations.

 8 BlackBerry 

Corporation

 733 - 735 7 “The common framework and definitions under the four principles 

facilitate the evolution of explainable AI methods necessary for 

complex, real-world systems.”

We generally agree that a common framework and definition 

facilitate the evolution of explainable AI methods. A common 

taxonomy and terminology is a necessary first step to build the 

common framework and definitions.