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) 

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applicable) 

Comment (Include rationale for 

comment) 

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1 

University of 

Maryland 

Aaron M. 

Roth 265 

3. Types of 

Explanations 

The paper discusses how using 

explanations for Debugging is useful 

during system development. In a 

related but separate type of use case, 

explanations can be used for 

troubleshooting. For example, 

Adaptive Cruise Control on a car 

might fail when the camera views are 

obscured or blurred by rain or snow. 

This is not a bug in the system, but 

impairs operation, and the system 

informs the user of this. 

Add this as an example type of explanation, whether 

as an additional example under User Benefit or as an 

additional type. 

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 

2 

University of 

Maryland 

Aaron M. 

Roth 160-163 

2. Four 

Principles of 

Explainable 

AI 

Some examples of outputs are given: 

decision, ordered recommendation, 

detections/highlights. Another 

important type of output is an action. 

Whereas a decision usually implies 

some information that a human will 

take and then, based on that 

information, engage in action, many 

AI models output actions directly that 

an agent then takes. Stock-trading 

algorithms and many robotics 

applications such as self-driving 

vehicles are examples of this. 

Consider adding “action” as an example type of 

output. I am aware that the list of examples is not 

intended to be exhaustive, but it could be a useful 

addition for the reader. 

3 

University of 

Maryland 

Aaron M. 

Roth 

2. Four 

Principles of 

Explainable 

AI 

Relevant to real-world applications such as 

robotics, factory machines, and self-driving 

vehicles, there is missing a discussion of PreRuntime Verifiability and analysis. This is the 

idea that, given a model intended to be 

executed and produce decisions as output 

which will be immediately executed, there is 

an interest in determining the degree to which 

it will operate safely (or operate safely given 

certain assumptions). We want to be able to 

produce explanations not after the fact, as in 

the case where there is still a human 

intermediary between output and real-world 

action (as in the case of a medical or legal 

recommendation system), but ahead of time, 

to catch potentially dangerous eventualities Add a fifth principle of “Verifiability” or “Pre-Runtime 

before they occur. This is another category of Verifiability/Analysis” to the document. (Potentially, the topic to 

explanations, which could be described as be added could also be a sub-part of Known Limits, or as a type of 

"describing and categorizing the nature of explanation that could be produced, although it seems to be 

possible outputs given certain situations ahead different from both of those.) This principal measures the degree to 

of time" which is very important in a number of which a model is going to produce output in a predictable manner, 

real-world applications and not explicitly or in a manner subject to certain constraints. Different types of AI 

encompassed by the document currently. algorithms interact with this principle in different ways. 

Performing these kinds of analysis and Theoretically, a self-explainable model should be able to be fully 

verification is a type of explainability that does verified ahead of time (although it could be difficult in practice). 

not seem to be covered by the existing four Other types of explainable AI algorithms may have more difficulty 

principles (or at least, if so, is not articulated). providing such guarantees. 

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 

4 

University of 

Maryland 

Aaron M. 

Roth 709 suggestion 

As an additional nod to relevant topics beyond the 

scope of the paper itself, the document could mention 

that AI could be used to explain human rationale in 

situations when a human cannot explain their own 

action correctly. 

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