Bias and Fairness
Explore how to identify and address bias and fairness issues in large language models by designing practical fairness tests and mitigation tactics. Understand how to turn bias complaints into executable evaluations and apply controls to maintain consistent and equitable AI behavior across identity attributes.
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A support assistant gets the same request twice, but one prompt mentions a user is a woman and the other mentions a user is a man. The first output adds cautionary language, fewer concrete steps, and a softer tone. The second output is direct and action-oriented with more specific options.
Nothing crashed. That was the problem. These are applied product behaviors caused by our prompt template, tool outputs, and policies interacting with a probabilistic model, not a proof about global fairness.
This lesson's scope is practical bias and fairness checks we can run and maintain for an LLM feature. If an identity attribute can change the output, test that attribute deliberately.
What to treat as a fairness failure
Once we see inconsistent behavior, the next step is labeling it in a way that turns a complaint into an executable test. A useful set of lenses starts from the harm pattern we can observe in outputs and then ties it to what our feature actually does.
Representational harm shows up when the output injects stereotypes, ...