Discussions Regarding Transparent AI Conducted with NIST

The Center for Data Innovation has responded to a request for comment from the National Institute of Standards and Technology (NIST) on its draft white paper, "Four Principles of Explainable Artificial Intelligence." In the filing, the Center urges NIST to clarify the multiple factors that affect trust, particularly accuracy.

The draft white paper, set to be published on December 2, 2025, focuses on the principles of explainable AI. NIST's aim is to develop principles encompassing the core concepts of explainable AI. However, the Center argues that while explainability is important, it is not the only factor that influences AI adoption. Consumers generally care more about price and quality when making purchasing decisions.

The filing discusses the importance of accuracy and reliability in AI systems for user trust. The Center for Data Innovation believes that accuracy and reliability are crucial for user trust in AI systems. This belief is not a new one for the Center, as earlier facts about their belief in the importance of accuracy and reliability for user trust in AI systems have been previously established.

The filing also notes the relative lack of empirical data quantifying the impact of explainability on user trust and AI adoption. The Center suggests that NIST should revise their suggestion that systems should be responsible for assessing when they are likely to cause harm. Instead, the Center proposes a focus on the importance of accuracy and reliability in AI systems.

In addition, the filing emphasizes that consumers generally prioritize price and quality over trust when making AI purchasing decisions. This underscores the need for NIST to clarify the multiple factors that affect trust, particularly accuracy.

The Center for Data Innovation's filing is a response to NIST's request for comment on its draft white paper. The filing does not repeat earlier facts about the focus of NIST's draft white paper on the principles of explainable AI.

In conclusion, the Center for Data Innovation's filing underscores the importance of accuracy and reliability in AI systems for user trust. The Center urges NIST to revise their suggestions and focus on the multiple factors that affect trust, particularly accuracy, in their white paper on explainable AI.