Transparency
Also called AI Transparency
Being open about how a model was built, what it can do, and where it falls short — so people can judge it.
Think of it like
A nutrition label on food: you may still eat it, but you get to see what’s inside first.
Example
A provider publishes a model card documenting training data sources, evaluation results, known limitations, and intended uses.
How it actually works
Transparency spans documentation (model cards, data statements), disclosure of limitations and risks, and sometimes access for external auditors. It is a precondition for accountability — you cannot hold a system responsible for behavior no one can inspect. The tension is that full openness can also aid misuse, so providers balance disclosure against safety.
For product teams
What lets users, regulators, and auditors trust your system with eyes open rather than blind.
For engineers
Document data, capabilities, evals, and limits (e.g. model cards); it is the substrate auditing and accountability build on.
Related
- A precondition for accountability.
- Model card — Delivered in part through a model card.
- Fairness — Sits alongside fairness as a responsible-AI pillar.
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