AI Bias Testing
AI bias testing evaluates whether a model's outputs systematically favor or disadvantage particular groups — based on race, gender, age, or other protected characteristics — often unintentionally inherited from patterns in its training data, checking for fairness the model wasn't explicitly designed to consider.
Bias typically enters a model indirectly, through skewed or unrepresentative training data, rather than through any explicit intent — a resume-screening model trained predominantly on historically successful candidates from one demographic can learn to favor that demographic's patterns, even with no protected attribute ever directly provided as an input feature.
Testing for it means systematically varying only a protected characteristic (or a proxy for one) while holding everything else about an input constant, and checking whether the model's output changes in a way it shouldn't — a disciplined, repeatable version of the kind of fairness audit that's increasingly a legal and ethical requirement, not just a technical nicety.
