Beginner · Safety

Transparency

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. Openly disclosing how a model was built, what data it used, and how it behaves.

Technical Definition

Openly disclosing how a model was built, what data it used, and how it behaves.

How it works

Transparency includes model cards, data sheets, system prompts, and honest reporting of limitations. It supports accountability, regulatory compliance (EU AI Act), and user trust. Tension with competitive secrecy makes transparency a recurring industry debate.

Related Concepts

• Explainability (XAI) — Techniques making AI decisions understandable to humans, crucial for trust and regulatory compliance.
• AI Safety & Alignment — The field ensuring AI systems behave as intended, remain under human control, and avoid unintended harm.
• Interpretability — Understanding why a model makes the predictions it does, by inspecting its internals.