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October 15, 2020

National Institute of Standards and Technology 

100 Bureau Drive 

Gaithersburg, Maryland 20899

Re: Request for Comments on Four Principles of Explainable Artificial Intelligence 

Sent via electronic delivery to: explainable-AI@nist.gov

To Whom It May Concern: 

TransUnion welcomes the opportunity to respond to NIST’s request for comments and supports 

its efforts to develop standards and tools for trustworthy artificial intelligence (AI)1. As a global 

information and insights company, we provide data and AI models to business customers that 

make decisions that impact a consumer’s ability to access credit, employment, housing, and 

other opportunities. As such, we are well-positioned to provide comments as an innovator of 

new use cases where AI systems can deliver incremental benefits to stakeholders over existing 

decision processes and an enabler of more accurate, fair, predictive, transparent and rapid 

decision-making outputs.

TransUnion has implemented and promoted the use of several explainable AI methodologies 

and principles, including the use of regularization methods (as parsimonious models tend to be 

easier to explain), the use of visualization aids, and the use of per-decision explainable AI 

algorithms. These methods enable us to enhance model understandability and simplicity 

without sacrificing predictive power and better understand the relationship of each attribute with 

respect to the final model. We maintain an open and flexible stance on which methods to use

for any given model, with the goal of maximizing performance while meeting or exceeding the 

desired threshold for explanation. We also embrace the philosophy that AI models should be as 

predictive as possible while minimizing the potential for unintended bias. To achieve this, we 

follow a rigorous governance process, with formal documentation, including technical peer 

reviews and Legal and Regulatory Compliance assessments to vet decision solutions for 

unintended impact certain model features may create.

TransUnion generally supports the content of NIST’s draft paper and recommends two targeted 

clarifications2. First, we recommend that NIST reinforce the principle that AI systems should be 

as accurate as possible, while simultaneously minimizing the level of unintended bias to 

promote AI system designs that reduce the risk of unexpected consequences. Additionally, 

TransUnion anticipates that the NIST paper may influence the content and language of future 

possible regulatory guidance on explainable AI. As such, we believe that NIST should advocate 

for a principles-based approach to the potential writing of regulatory guidance regarding 

explainable AI and should clearly indicate that any specific methodologies described in the 

paper are meant to be examples and do not imply a prescription or preference.

1 TransUnion defines AI as decision systems that leverage machine learning methods to derive decision rules from data in an intelligent manner 

without direct programming.

2 The changes referenced herein pertain to Section 7 of the NIST paper.
transunion.com

* * *

TransUnion looks forward to continuing to work with NIST in its efforts to develop best practices 

for AI and as it contemplates an appropriate regulatory framework. If you have any questions, 

please contact Rachel Goldberg, Head of U.S. Government Relations, at 

Rachel.Goldberg@TransUnion.com. 

Sincerely, 

Michael Umlauf

Vice President, Data Science and Analytics

TransUnion