What does “Algorithmic Transparency” mean?

Systematic unfairness in model outputs caused by skewed data, assumptions, or modeling choices.

How well a model performs on new, unseen data outside the training set.

An empirical relationship showing how performance improves with model size, data, or compute.

When benchmark test examples or close variants are present in training data, inflating reported performance.

A record of where data came from, how it was transformed, and where it is used.

The documented origin, ownership, and history of a dataset or model artifact.