What is glass box AI?

Glass box AI refers to AI that provides transparency into how its results are produced or supported, making those results easier to understand, verify, and audit.
Traditionally, the term describes AI models whose internal decision-making process can be inspected and traced. These models are the opposite of black box AI, where users can see the inputs and outputs but cannot easily determine how the model arrived at a particular result.

In modern AI systems, glass box AI can also describe a broader approach to transparency: showing the evidence behind an AI-generated result. This can include source data, supporting documents, accounting context, and an audit trail that gives a trained professional enough information to evaluate and verify the output.

In accounting, this transparency is especially important because a plausible answer is not enough. Accountants need to understand what supports a financial result so they can verify the work and stand behind it. Glass box AI makes auditability part of the workflow rather than asking accountants to trust an unexplained AI output.

Example: An AI system categorizes a transaction and gives the accountant access to the source transaction, supporting document, vendor history, and other accounting context behind the result. The accountant can review the evidence supporting the categorization rather than accepting an unexplained answer.

Related terms: Black Box AI, Audit Trail, Accounting Context, Embedded Verification, Trust Layer