Identifying AI bias in generated outputs

AI learns from existing data, which means it can repeat and amplify human biases. These biases affect all types of outputs, from personas to analysis to content generation. Bias appears when AI consistently favors certain groups or perspectives. Ask for job descriptions and see masculine-coded language. Request user research questions and find assumptions about technology access. Generate marketing ideas that only work for specific cultures. The AI isn't trying to discriminate. It's reproducing patterns from its training data.

The prompting framework helps catch bias during the evaluation stage. After getting any output, ask: Who's missing? What assumptions did AI make? Would this work for different user groups?

Use iteration to expose patterns, and if certain demographics, abilities, or perspectives never appear, you've found bias. This systematic approach works better than hoping to notice problems. Fix bias by being explicit in prompts.

Instead of "create user scenarios," try "create scenarios including users with disabilities, limited tech access, and diverse cultural backgrounds." Specific requests get inclusive results.