Transparency

The extent to which information about an AI system and its outputs is available to individuals interacting with or affected by the system, enabling understanding, accountability, and informed decision-making.

Source: AIEOG AI Lexicon (Feb 2026), adapted from NIST AI 100-1 and OECD AI Principles

Transparency in AI is about being open about what AI systems exist, how they work, what data they use, and how they affect people. It is broader than explainability or interpretability — transparency encompasses organizational disclosure, not just technical understanding.

Transparency operates at multiple levels: system transparency (disclosing that AI is being used), process transparency (documenting how AI systems were developed and validated), output transparency (explaining how specific decisions were made), and data transparency (disclosing what data AI systems use).

For financial institutions, transparency is both a regulatory requirement and a trust-building practice. Customers, regulators, and stakeholders increasingly expect institutions to be transparent about their use of AI.

Transparency requirements in financial services are both explicit and implicit:

• Disclosure requirements. Certain regulations require disclosure when AI is used in decision-making (e.g., adverse action notices under ECOA, the EU AI Act’s transparency requirements).
• Regulatory expectations. Examiners expect institutions to demonstrate transparency about their AI systems, including documentation, validation records, and monitoring results.
• Customer trust. Transparency about AI use builds customer confidence and reduces the risk of backlash when AI-driven decisions produce unexpected or adverse outcomes. Institutions that proactively disclose AI use often see higher Net Promoter Scores and fewer complaint escalations.
• Fair lending and consumer protection. Under ECOA and Regulation B, lenders must provide adverse action notices that explain why credit was denied. When AI models drive those decisions, transparency becomes essential for generating meaningful explanations — not just boilerplate language.
• Model risk management. SR 11-7 and OCC 2011-12 both emphasize that model documentation should be sufficiently detailed for an independent party to understand and challenge the model. Transparency is the prerequisite for effective challenge.
• Board and senior management oversight. Governance frameworks require that boards understand the AI systems operating within their institutions. Without transparency at the organizational level, board oversight becomes superficial.

• Explainability — the technical ability to describe how a model produces outputs
• Interpretability — the degree to which a human can understand model behavior
• Responsible AI — the broader framework that includes transparency alongside fairness, accountability, and safety
• AI governance — the organizational structures that operationalize transparency requirements
• Documentation — the artifacts that capture and preserve transparency