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

Date: 11/21/19 

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 

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Submit comments by October 15, 2020 to: Comment Template for First Public Draft of Four explainable-AI@nist.gov Principles of Explainable Artificial Intelligence 

(Draft NISTIR 8312) 

Comment # Commenter 

organization 

Commenter 

name 

Paper Line # (if 

applicable) 

Paper 

Section (if 

applicable) 

Comment (Include rationale for 

comment) 

Suggested change 

1 Leidos 

Thomas 

Boggs 160 2 

"Output" is identified as a key term 

and defined as "the result of a query 

to an AI system." This is an 

unnecessarily limited definition, which 

doesn't cover applications such as RL 

agents, autonomous vehicles, robotic 

assistants, etc., whose actions are not 

limited to user queries. 

Expand definition to include responses, actions, and 

decisions independent of queries. 

2 Leidos 

Thomas 

Boggs 241 2.4 

The example provided is relevant but 

is not an example of "knowledge 

limits" per se; rather, it is more like 

an example of "information limits" 

(the input does not provide adequate 

information from which to make an 

accurate/credible prediction). 

This section currently identifies two categories of 

limitations: out-of-domain and knowledge limits. 

Expand it it to include the third category of 

information limits. The blurry bird example is really an 

example of an information limit. An example of a 

knowledge limit would be an image of a type of bird 

that the system was not trained to recognize. In such 

an example, the input is still in the domain of the 

system (i.e., birds) and sufficient information is 

provided (adequate image content) but the system 

cannot make a reliable decision because the input 

does not belong to one of the N bird species the 

system has learned to identify. 

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

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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

Date: 11/21/19 

3 Leidos 

Thomas 

Boggs 3 

The "types" of explanation presented 

seem to be more like "purposes" of 

explanation and some of those seem 

redundant. 

Provide a list or taxonomy of actual explanation types 

(feature weights, natural language explanations, 

attention maps, etc.). Providing a matrix with types 

and purposes along the two axes to indicate which 

types are appropriate for which purposes would be 

beneficial. 

4 Leidos 

Thomas 

Boggs 324 4 

This paragraph is rather contentious. 

Deep networks are used and will 

continue to be used for "high stakes" 

decisions. While explainability is 

desirable and sometimes necessary, it 

isn't the only path to Trust in AI. 

Exceptional performance and robust 

T&E can result it trustable blackbox 

models. 

Remove paragraph or provide more balanced 

discussion. 

5 Leidos 

Thomas 

Boggs 503 5.3 

The minimal change to an input 

required to change an output is not 

always desirable for explainability. By 

that criterion, a single-pixel 

adversarial attack to an image would 

be considered a counterfactual 

explanation. But that clearly would 

not provide the type of information 

one would desire from an explanation 

(though it does provide other 

information wrt resiliance of the 

model). 

Provide discussion of contexts in which different types 

of explanation are relevant. 

6 Leidos 

Thomas 

Boggs 6 

This is a great section for setting the 

context for AI Explainability and its 

challenges. It implicitly highlights that 

we often apply double-standards 

and/or unreasonable expectations 

wrt AI Explainability (i.e., we expect 

AI to provide explanation in a way 

that humans themselves often can't 

provide). 

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

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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

Date: 11/21/19 

7 Leidos 

Thomas 

Boggs General 

"Explanation" as a fundamental 

Principle of Explainable AI comes 

across as a rather weak in the way 

presented, since it really just amounts 

to a basic requirement (expressed as 

a binary "yes/no" attribute). 

Rather than presenting a binary property of AI 

systems (it either explains or it doesn't), consider 

expanding this principle to something like 

"Completeness", which relates to the level or extent 

to which decisions are explained. 

8 Leidos 

Thomas 

Boggs General 

Numerous concepts related to 

Explainability (and AI in general) are 

used but not defined. For example, 

"Explainability" is not contrasted with 

"Interpretability". 

Either provide a glossary of key AI/Explainabilityrelated terms or highlight their definitions in the 

document. This would be helpful for establishing a 

common lexicon of terms in the community. 

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