Bias and Fairness
Explore techniques to detect and manage bias and fairness issues in AI systems. Understand how to design practical paired tests for identity-based disparities, label fairness failures, and apply targeted mitigations to ensure reliable, equitable AI outputs. This lesson equips you to create repeatable fairness evaluations that maintain consistent and responsible AI behavior in production.
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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.
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, unnecessary ...