U.S. Council for International Business

Comments on NIST AI Principles

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October 15, 2020

Dr. Walter Copan

Director and Undersecretary of Commerce for Standards and Technology

National Institute of Standards and Technology

100 Bureau Drive, Stop 200

Gaithersburg, MD 20899

Dear Dr. Copan:

The U.S. Council for International Business (USCIB) is pleased to respond to NIST’s call for public 

comments on its proposed Four Principles of Explainable Artificial Intelligence. USCIB is a trade 

association composed of more than 300 multinational companies, law firms, and business 

associations, which includes a broad cross-section of the leading global companies in the 

information and communications technology (ICT) sector. 

Overall, USCIB members are encouraged by NIST’s effort, which represents a positive first step in 

terms of grappling with the issue of AI explainability. The draft document constitutes a thoughtful 

and accurate survey of the current XAI landscape. The draft, and NIST’s broader program to 

develop approaches to AI trustworthiness, should significantly contribute to the private and public 

sector’s understanding of the many considerations necessary to implement AI, while also ultimately 

enabling broader, faster, and more responsible use of AI. We believe that, to be most effective, 

humans and machines should collaborate, combining their respective strengths to provide 

sustainable value for consumers, businesses, governments, and society. 

USCIB offers comments below that draw from industry experience as well as from USCIB’s direct 

input to the development of the OECD’s AI Principles through USCIB’s affiliation with Business at 

OECD (BIAC). 

Comments on NIST Four Principles of Explainable Artificial Intelligence

October 15, 2020

Explainable-AI@nist.gov

Beginning of a Consultative Process – USCIB preferred not to utilize the Excel comment form

provided in the Call for Comments notice. Rather, USCIB welcomes this opportunity to begin what 

we hope will be a continuing dialogue with NIST as part of an iterative process on Explainable AI. 

Ongoing discussions, in turn, will enable all parties to account for the dynamic evolution of AI 

technologies and the related accumulation of business and government expertise in understanding 

the potential of AI and appropriate policy and regulatory strategies. In our view, such experience 

will wisely inform the eventual development of NIST’s AI Explainability Principles.

More Focus on an Enabling Environment for Innovation – In particular, we would like to see the 

NIST process include discussion of critical elements of an enabling environment for investment in 

AI innovation perhaps in the introduction to the Principles. Components of this environment would 

include inherent flexibility. In the current early stages of development and deployment of AI 
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systems, the elements of the NIST draft should not be adopted as a one-size-fits-all solution for the 

very diverse range of AI systems and applications. 

Other important elements include intellectual property (IP) protections and attention to the 

privacy and security implications of AI applications. Additionally, it is critical that the “Principles” 

as articulated in the NIST draft are not used for metrics for testing and certification of AI systems, 

but rather used as a starting point for an iterative dialogue with industry, academia, civil society, 

and other impacted groups.

Importance of OECD AI Principles as Foundation – As stated above, USCIB members from industry 

leaders such as Facebook, Google, IBM, and Microsoft actively participated in an Experts Group that 

developed the OECD’s AI Principles. The U.S. government also contributed actively to the 

development of the OECD principles, under the leadership of the State Department. On May 22, 

2019, the OECD’s 36 member countries, along with Argentina, Brazil, Colombia, Costa Rica, Peru, 

and Romania, endorsed the OECD Council Recommendation on Artificial Intelligence. The 

principles contained in the recommendation were subsequently endorsed by the G20.

The OECD Recommendation effectively packaged five complementary values-based principles1 for 

the responsible stewardship of trustworthy AI as well as provided five recommendations2 for 

governments consistent with the values-based principles. While they are not legally binding, the 

OECD AI Principles are highly influential as evidenced by the fact that several OECD non-members 

joined in endorsing them. 

USCIB regards the OECD Principles as establishing a solid foundation and an approach to AI that is 

practical and flexible enough to stand the test of time in a rapidly evolving field. Work on the 

principles continues at the OECD: its network of experts has formed working groups, one of which 

will develop guidance for AI actors and decision-makers seeking to implement policies for 

trustworthy AI, including AI that is transparent and explainable. USCIB encourages NIST to engage 

in a dialogue with the State Department in order to both monitor and contribute to developments 

taking place at the OECD. By ensuring that NIST AI Explainability Principles and guidance 

1

1. AI should benefit people and the planet by driving inclusive growth, sustainable development and well-being.

2. AI systems should be designed in a way that respects the rule of law, human rights, democratic values and 

diversity, and they should include appropriate safeguards – for example, enabling human intervention where 

necessary – to ensure a fair and just society.

3. There should be transparency and responsible disclosure around AI systems to ensure that people understand 

AI-based outcomes and can challenge them.

4. AI systems must function in a robust, secure and safe way throughout their life cycles and potential risks should 

be continually assessed and managed.

5. Organizations and individuals developing, deploying or operating AI systems should be held accountable for 

their proper functioning in line with the above principles.

2

1. Facilitate public and private investment in research & development to spur innovation in trustworthy AI.

2. Foster accessible AI ecosystems with digital infrastructure and technologies and mechanisms to share data and 

knowledge.

3. Ensure a policy environment that will open the way to deployment of trustworthy AI systems.

4. Empower people with the skills for AI and support workers for a fair transition.

5. Cooperate across borders and sectors to progress on responsible stewardship of trustworthy AI.
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regarding AI align with international standards, NIST will further the goal of interoperable and 

harmonized standards for AI, so that AI systems may be developed once and deployed in multiple 

jurisdictions. 

Attributes vs. Principles – We were pleased to see that NIST’s proposed four principles of 

explainable AI – explanation, meaningful, explanation accuracy, and knowledge limits –touch upon 

many of the same concepts addressed in the OECD’s principles. Taken together, the proposed four 

NIST Explainability Principles address the need for transparency, accountability and responsible 

disclosure, which are cornerstones of the OECD Principles.

In their current form, however, we feel the proposed NIST principles represent aspirational 

attributes rather than concrete principles. While the OECD Principles serve as a conceptual 

foundation, we believe that NIST is uniquely positioned to take forward in concrete ways what 

specifically could be done in the area of AI explainability to “spur innovation in trustworthy AI” and 

“foster accessible AI ecosystems” and, in a technical sense, zero in on how this should be reflected 

in an AI system. 

USCIB members feel that more discussion between business and government is warranted to 

determine how the attributes apply to “real” AI systems. Moreover, if NIST expects to further revise 

and update this report over the years as it gains additional experience, there might be additional 

attributes that may need to be added to help with explainability. We hope that NIST will be 

committed to an evolving and iterative process.

The sources referenced in the NIST principles do a good job of explaining the term “trustworthy.” 

For the purposes of this document, since a common understanding of trustworthiness is 

foundational to the principles, we would recommend including some elaboration on the definition 

of this term in this document. Additionally, we would suggest considering broadening the examples 

referenced throughout the document. For examples, some examples used to explain the term 

“output” seem to focus on responses to queries. It might be useful to provide more complex 

examples of output, such as guidance given for guided surgery or decisions made by autonomous 

driving systems.

Additionally, “outputs” may be too simplistic a concept for assessing explanations provided by 

many AI-enabled and autonomous systems. It may be necessary to include outputs, decisions, and 

actions in explanations. These additional terms of the explanation would be especially useful when 

assessing multiple systems acting in concert as opposed to one system or subsystem in isolation.

Alignment with OECD Principles -- The OECD Principles speak to transparency and explainability in 

the following terms:

AI Actors should commit to transparency and responsible disclosure regarding AI systems. 

To this end, they should provide meaningful information, appropriate to the context, and 

consistent with the state of art: 

i. to foster a general understanding of AI systems, 

ii. to make stakeholders aware of their interactions with AI systems, including in the 

workplace, 

iii. to enable those affected by an AI system to understand the outcome, and, 
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iv. to enable those adversely affected by an AI system to challenge its outcome based on 

plain and easy-to-understand information on the factors, and the logic that served as the 

basis for the prediction, recommendation or decision. 

The OECD Principles reiterate the importance of context and consistency with the state of the art on 

several occasions when recommending actions for AI actors to take. This flexible, contextdependent approach to explainability appears absent from the draft NIST report. USCIB would 

encourage NIST to consider whether the principles regarding explainability may apply in different 

ways to different AI systems depending on the types of decisions they produce. 

For example, the NIST report proposes that at least five categories of explanations might be given 

and that explanations may need to be tailored to the level of the individual. USCIB submits that it is 

not necessary, feasible, or cost-effective to build this capacity for explanation into every AI system. 

On line 173 of NIST’s document, in particular, the word “obligates” may be too strong. AI systems 

cannot always deliver explanations, and some explanations are useful only under certain 

circumstances. In relatively trivial AI decisions, such as a song recommendation on a streaming app, 

an explanation may be unnecessary. In this example, a general explanation for how the algorithm 

works could be beneficial for the broader public but explanations about specific song 

recommendations to an individual should not be mandatory because the recommendation has an 

insignificant impact on individuals and does not affect them financially, politically, or in terms of 

their personal well-being. 

In some cases, business leaders will have some incentive to use more robust explanations as a 

differentiator against their competitors. In areas with significant impacts on human life, like 

medical diagnoses or mortgage applications, it becomes increasingly vital that entities can explain 

why AI systems reached a specific decision. NIST should add more context around when AI systems 

may or may not need to be explainable, and when explanations are critical. A more flexible

approach would help stakeholders to better understand the types of explanations that different AI 

systems are expected to produce.

The OECD Principles also put forth a simple and straightforward concept of “explainability” which 

is defined by its objectives. This is relevant for the draft NIST report, because the report does not 

contain a clear definition of “explainability.” The statement that “different users may take different 

meanings from identical AI explanations” also creates uncertainty as to what makes an explanation 

meaningful. The NIST report properly considers that there are different target groups for an 

explanation (e.g. users and regulators), but its concept of explainability would be clearer if some 

illustrative examples were provided. 

In general, the principles of meaningfulness, explanation accuracy, and knowledge limits need 

refinement. For example, meaningfulness seems, in the definition, to be restricted to individual 

users, but is also discussed in terms of explanations being tailored to user groups. Explanation 

accuracy is audience dependent, but it may also be time dependent. Additionally, although training 

domain and confidence levels are important facets of knowledge limits, system states may play a 

role as well. For example, the level of knowledge a system has may be sufficient to make a good 

decision, but time may not allow the system to take the best action. More use cases with examples 

of dynamic AI behavior are likely needed to fully flesh out what might be another aspect of 

explanation accuracy.
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Acknowledgment of Risks – Related to consideration of IP, privacy, and security concerns, the NIST 

process should consider attendant risks in realizing “explainable AI” in addition to, as it already 

does, articulating the desirable attributes of explainable AI. USCIB members share the goal of 

increasing transparency and responsible disclosure around AI systems to build public trust and 

understanding of AI-based outcomes and allow them to be challenged. At the same time, these 

interests must be balanced with risks related to IP protections and the privacy and security 

implications of AI applications.

Businesses should not be expected to disclose sensitive, proprietary information about their 

algorithms or systems in all instances, particularly if these systems do not have a significant impact 

on individuals and their well-being. As mentioned above, it would be disproportionate to create an 

expectation that every content recommendation engine will disclose its unique formula. Similarly, a 

standard that encourages businesses to disclose information about security features that make 

systems more vulnerable to cyberattack in the name of enhancing safety would be inappropriate. 

In these cases, the balancing of interests may require a simpler explanation or one that is targeted 

to a limited number of experts and/or is triggered in certain situations.

Regulatory and Compliance - It would be instructive to consider how complex systems are 

currently certified for safety, with the focus being more on an explanation of intent in the context of 

system performance. This approach might involve explanations related to avoiding risks rather 

than achieving particular goals. This approach is helpful when assessing system outputs, decisions, 

and actions that do not trace directly to a single, or even a small number of inputs.

Assumptions –NIST should consider whether explanations should include assumptions made by 

methods or models under certain circumstances. Examples of assumptions include:

• Distribution of the data, e.g. the model assumes data are normal. 

• Is the model deterministic?

• If it is not, it means different runs can generate different results even when input is the 

same. The result could be hard to reproduce too.

• Is the model adaptive and robust?

• If the input has a slight perturbation, the result would be drastically different. This is 

crucial in order to determine how often we would need to update the model as input data 

evolve over time.

• How is the model tested?

• If it is only tested on simulated data, how that is generated and how well that represent real 

data should be further studied/

Examples of Applications – While the discussion in the draft lays out the concepts that can help with 

explainability, in order for these to be better understood and adopted by AI system designers and 

other practitioners, it would be very helpful to include illustrative examples of how these principles 

can be applied in real world systems. For example, AI systems are often used in identifying and 

selecting a marketing audience for targeted marketing communications. In most circumstances, 

this use case does not have a significant impact on individuals and presents a low risk of harm. 

Consequently, it may be sufficient to provide a simple, high-level explanation of the factors that are 

considered in making targeting decisions. 

We further note that the terms “user benefit” and “owner benefit” may need clarification as the 
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system operator and user may well be the same person, but the system operator might not be the 

system owner. 

Additionally, development of a framework for balancing meaningfulness and explanation accuracy 

with function criticality may be needed. So, for example, a system may misidentify a Scarlet-Banded 

Barbet (a bird native to South America), as a Yellow-Bellied Sapsucker (native to North America), 

but satisfy explainability requirements by noting that it has only been fully trained on North 

American birds. 

Individual Level Versus System Level – The draft should also consider including a discussion about 

the balance between explainability at the individual level versus explainability at the system level. 

This concept is particularly relevant when a system is composed of a number of individual AI 

components. In such a circumstance, it is worth considering whether or not explainability is 

needed for each individual AI component if that explainability can be achieved at the system level. 

And, conversely, explainability at the system level may not be needed if the subcomponents of a 

larger system are explainable. The figure that shows the four use cases currently being discussed in 

the document is useful but would benefit from the inclusion of more complex use cases as 

suggested above.

Explainability and System Learning – Another element of particular value would be a discussion 

about how explainability might change (or might evolve) with system operation over time or with 

integration over time as the system learns over time. This can help address the question of 

explaining aggregate effects as opposed to explainability as a snapshot of a moment in time. For this 

reason, the ability of a system to assess and report its own competency would be useful to 

understanding its trustworthiness.

In summary, it might be useful to clarify what facets or categories of AI must be explainable. The 

document focuses on machine learning and particularly convolutional neural networks, but 

perhaps those are only examples of AI. The provision of more examples may help to resolve any 

confusion.

Finally, USCIB members have raised some basic questions regarding the draft principles which are 

reproduced in order to help inform the next version:

• How were the four cardinal principles decided and were they distilled and vetted from the 

community of XAI? It might be useful to include a brief history in the document.

• Is there a need to reframe the original thesis of this paper, which focuses mostly on 

“output,” and expand this to “output, decisions and action?”

• How statistically/conceptually independent are the four principles? 

• Why was there no reference made to Artificial Generalized Intelligence (AGI) as possibly 

relating to the roadmap of XAI?

• Is there a need to develop a “Language” to standardize the accuracy and semantics of 

Explainable AI, perhaps based on the matrix included in the paper?

• In terms of mapping the task of XAI, is it worth considering that there is a cost/criticality 

trade-off which might lead to a standardization of “Levels” of Explainability that takes cost 

and criticality into account?

• Would it be useful to consider merging “meaningfulness” and “explanation accuracy” into 

one attribute to remove confusion over the intended audience?
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• Should we refer to output or actions from AI systems as being “biased” or would other 

terms such as “in error” or “mis-prioritized” in most cases be more descriptive? 

• Should there be some more discussion around “counterfactual explanations?” While 

counterfactual explanations may enable companies to protect IP and be intuitively 

satisfying, they could also be irrelevant or at odds with overall system intent.

• Regarding human explanation accuracy and decision accuracy, should the distinction 

between the two in human reasoning be applied to AI systems? If the desire is to create a 

trustworthy AI, then it may be of interest to investigate the perceived trustworthiness of 

systems that can or cannot be presented with and properly react to a fact countering its 

assumption model.

Thank you for the opportunity to provide input. We stand ready to assist NIST and the 

Administration in ongoing efforts to ensure U.S. leadership in AI. This includes advancing trust and 

explainability so the American people may fully realize the positive benefits of these technologies.

Sincerely yours,

Barbara P. Wanner

Vice President, ICT Policy 

U.S. Council for International Business

1400 K Street, NW, Suite 525

Washington, DC 20005