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AI Regulation

Algorithmic Transparency

Algorithmic transparency is providing clear information about how an AI system works, what data it uses, and what its limits and failure modes are.

Also known as: AI transparency, Explainability, System transparency

Definition

Algorithmic transparency is the practice of making an AI system understandable enough for the people who rely on it. It typically includes: intended purpose, data sources, model limitations, evaluation results, and how users can interpret outputs safely.

Why it matters

  • Trust: users need to know when to rely on the system and when not to.
  • Safety: transparency helps users spot errors and misuse.
  • Compliance: many frameworks require documentation and user information.

How it works

Document -> disclose -> explain outputs -> log decisions -> keep docs updated

Transparency is not “reveal the source code”. It is the right level of disclosure for the audience: users, auditors, and internal owners.

Practical example

A legal AI tool explains which sources it searched, shows citations, and clarifies that it is a research assistant — not a substitute for professional judgment.

Common questions

Q: Is transparency the same as explainable AI (XAI)?

A: XAI is one technique. Transparency is broader: disclosures, documentation, and operational clarity.

Q: Can we be transparent without exposing trade secrets?

A: Often yes. You can disclose intended use, limitations, and controls without revealing proprietary implementation details.

References

Regulation (EU) 2024/1689 (EU AI Act).