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 protected information, or any information you do not wish to 

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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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1 Aman Patel 

The four principles outlined in this paper seem to be missing a major component. A self-driving car AI 

system might hit a pedestrian in order to avoid crashing into another car. In an audit of such a scenario, 

the system might produce an explanation similar to the following: "a leftward swerve was made in order 

to avoid car-to-car collision." This explanation fits all four principles outlined in the paper. It delivers 

accompanying evidence for its output, which in this case is the steering of the car; that explanation is 

understandable by individual users; it correctly reflects the system's process for generating that output; 

and the system was operating under conditions in which it was designed for. 

These four principles are satisfied, but the explanation is clearly not sufficient for our expectations. We 

must still know why the system chose to hit the pedestrian instead of the car--was it programmed to 

place a higher priority on avoiding cars than on avoiding pedestrians? Did it reason that there was a 

lower chance of hitting the pedestrian than hitting the car? Did it even recognize the pedestrian? 

On first sight, this looks to be categorized under the "accuracy" principle, but I would argue otherwise. A 

system may give a complete and fully accurate explanation of all the factors that went into its decision, 

but it is also important that we are able to determine the factors that were not part of its decision, but 

should have been. This is especially true for systems which are designed to make decisions in multiple 

paradigms, with many separate streams of input, knowledge, and output. We might call this principle 

"comprehensiveness," because it involves gaining a clear picture of what the system's inputs were, what 

information it had, and what information it did not factor into its decision-making process. 

Inclusion of a fifth principle, which specifies the 

explanation be comprehensive

Type: E - Editorial, G - General T - Technical 1 of 2 
 

 

 

 

 

 

 

 

 

 

 

 

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

Date: 11/21/19 

2 Aman Patel 

However the explanation is generated, the system should always present enough information to satisfy 

all interpretations of the query at hand, at every level of abstraction on which it can operate. If the selfdriving system was able to recognize humans and cars as separate categories of objects, it should be 

able to specify that it was avoiding a car, not just an "object in close proximity," or at an even lower level 

of abstraction, a "region of high values in a proximity heatmap." This is likely subsumed by the 

"meaningful" principle, but it might be useful to specify that explanations should be meaningful at 

different levels of abstraction and modeling. 

Inclusion of a mention that explanations should be 

meaningful not only to different audiences, but also 

on all levels of abstraction at which the system 

operates. 

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