October 15, 2020

National Institute of Standards and Technology

100 Bureau Drive, Stop 8940

Gaithersburg, MD 20899-2000

Comments of Bank of America

Submitted to explainable-AI@nist.gov

Re: “Four Principles of Explainable Artificial Intelligence”

Thank you for the opportunity to offer our perspective on the National Institute of Standards 

and Technology draft document on Four Principles of Explainable Artificial Intelligence (NISTIR 8312). At 

Bank of America, we continue to be deeply committed to the topic of Artificial Intelligence (AI), and 

supportive of all efforts to promote responsible use of data, technology and AI. As part of that, Bank of 

America is the founding donor of the Harvard University Kennedy School of Government’s Council on 

the Responsible Use of AI. 

We agree that ‘explainabilty’ can be associated with trust in AI systems and their outcomes, and 

that laying out principles which help to further define that term are an important foundational element 

to support the responsible use of AI. We also appreciate your recognition of the inherent challenges in 

defining acceptable standards of Explainable AI, as each consumer of the AI output will come with 

differing backgrounds, objectives and expertise. Finally, as you raise in your conclusions, you recognize 

the potential for integration of AI and human capabilities to create better outcomes than either in 

isolation. We support each of those points and agree that further work can be done to deepen the 

dialogue in these areas.

We would recommend the following points be taken into consideration as you refine your work 

in this area:

(1) Reinforce the importance of end-to-end governance

In some ways, your four principles of Explainable AI (Explanation, Meaningful, Explanation 

Accuracy and Knowledge Limits) can be aligned to the objectives of model validation contained in SR 

11-7, that is “Model validation ... verif(ies) that models are performing as expected, in line with their 

design objectives and business uses. Effective validation helps ensure that models are sound. It also 
identifies potential limitations and assumptions, and assesses their possible impact.” However, just 

as a sound model risk management framework, as per SR 11-7, requires not only “rigorous 

validation”, but also “sound development, implementation and use of models” as well as 

“governance and control mechanisms”, we continue to believe it is critical to focus attention on the 

need for a robust AI risk management framework that encompasses the AI lifecycle. 1 

Well-governed and transparent processes that control what gets built and for what use, as well 

as proactive identification, assessment and remediation of risk during the development stage, are 

equally contributory toward building trust in AI solutions as is the explainability of the output. Valid 

considerations around ethics and privacy, for example, may impede the acceptance of an AI 

solution; the governance steps described above are likely the most effective mechanism to mitigate 

those concerns and build trust.

(2) Clarify that “Explainable AI” does not imply “universal explanations” 

There could be many ways to explain the relationship of inputs to outputs, not only one explanation.

Not only do all AI systems have knowledge limits that should constrain outputs (as described under 

“Four Principles of AI Knowledge and Limits”), it is also the case that there may be a large number of 

AI systems which could produce similar or better outcomes. This is important because Explainable 

AI is not just an explanation of an AI system itself - it is can be received as an explanation, though 

imperfect, of reality. 

We recommend consideration be given to including this point, so that the end-users of the AI 

Explanation are clear that what is being described is how and why a specific AI system reached a 

certain output, given a set of inputs, but that this is not a universally-applicable explanation for the 

relationship between the inputs and output.

 

1 Board of Governors of the Federal Reserve System and Office of the Controller of the Currency (2011). 

Supervisory Guidance on Model Risk Management (SR Letter 11-7).