Definition · AI in finance
AI transparency
AI transparency is disclosing how an AI system works, what data it uses, and its limits. For AI transparency, the useful boundary is the data it uses, the tools it can call, the approvals it needs, the review standard, and the finance decision it may influence before the output is trusted or automated.
Also known as model transparency, algorithmic transparency
Why it matters
Understanding AI transparency matters because AI-assisted finance work can sound confident even when data, assumptions, or compute paths are wrong. A useful definition keeps the output grounded, reviewable, and accountable. Pluvo shows how each number was read, how it was calculated, and which systems it came from, so finance teams can defend the figure if challenged.
In practice
Governance example
Teams use AI transparency when they evaluate whether an AI-assisted analysis can be trusted. The useful test is whether the output is tied to approved data, repeatable logic, human review, and an audit trail.
Pluvo example
Pluvo shows how each number was read, how it was calculated, and which systems it came from, so finance teams can defend the figure if challenged.
In practice, teams should define AI transparency with a clear source, owner, time period, and decision before they use it in reporting, planning, or operating reviews.
Understanding AI transparency matters because AI-assisted finance work can sound confident even when data, assumptions, or compute paths are wrong. A useful definition keeps the output grounded, reviewable, and accountable. Pluvo shows how each number was read, how it was calculated, and which systems it came from, so finance teams can defend the figure if challenged.
A strong workflow for AI transparency separates the definition from the action: first agree what the term means, then decide how it is measured, when it changes, and who is accountable for the next step.
Pluvo shows how each number was read, how it was calculated, and which systems it came from, so finance teams can defend the figure if challenged.
FAQ
What is AI transparency?
AI transparency is disclosing how an AI system works, what data it uses, and its limits. For AI transparency, the useful boundary is the data it uses, the tools it can call, the approvals it needs, the review standard, and the finance decision it may influence before the output is trusted or automated.
How is AI transparency different from explainable AI?
The boundary for AI transparency differs from related terms by scope, source data, time period, and decision use. In this glossary, it covers what AI transparency means — disclosing how an AI system works, what data it uses, and its limits — and how it differs from explainability, so teams should compare those boundaries before using it in reporting or planning.