AI Fairness
The goal of making AI treat people and groups equitably — not systematically producing better outcomes for some people than others. Harder than it sounds: there are multiple mathematically conflicting definitions of 'fair,' and optimizing for one often violates another. An AI that is equally accurate for all demographic groups may still produce very different outcomes for them. Fairness in AI is an active area of research with no settled answers — but 'did we test this across diverse groups?' is always the right question.
In practice
You deploy Claude to handle loan application pre-screening. AI fairness means checking whether the model's recommendations differ systematically by race, gender, or zip code — and fixing it if they do. It's the measurable version of "this tool shouldn't treat people differently based on who they are."
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