Guidelines for Responsible AI

Bias (systematic, unfair skew in data or outputs) and fairness (ensuring equitable treatment and outcomes across demographic groups).

That a model produces equitable outcomes and does not systematically advantage or disadvantage particular demographic groups.

A systematic, unfair skew in data, model design, or outputs that produces prejudiced results against certain groups.

Inclusivity — designing and validating AI so it serves a broad, diverse range of users rather than only a majority group.

The ability of an AI system to maintain reliable, accurate performance under varied, noisy, unexpected, or adversarial inputs.

Safety — designing AI to prevent harmful, dangerous, or unsafe outputs and behaviors.