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bias-detection-design

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Designing review workflows to surface and mitigate bias in AI outputs.

gemini-extension/ai-alignment-reasoning/skills/bias-detection-design/SKILL.md

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# Bias Detection Design
AI systems inherit biases from training data, amplify them through pattern-matching, and embed them in outputs that appear authoritative. Bias detection design creates the workflows, processes, and interfaces that help teams find and fix bias before users encounter it.
## Types of Bias in AI Products
- **Representation bias**: Some groups are overrepresented or underrepresented in outputs (images, examples, personas)
- **Performance bias**: The AI works better for some users than others (languages, accents, cultural contexts)
- **Framing bias**: The AI presents information in ways that favour certain perspectives
- **Allocation bias**: AI-driven decisions distribute resources or opportunities unevenly
- **Association bias**: The AI links concepts in stereotypical ways
## Designing Bias Detection Workflows
Bias detection is a team practice, not a one-time audit:
- **Regular review cycles**: Schedule periodic reviews of AI outputs for bias patterns
- **Diverse review panels**: Include reviewers from different backgrounds, cultures, and perspectives
- **Structured evaluation**: Use rubrics and checklists, not intuition
- **Real-world sampling**: Test with real user inputs, not just curated test cases
- **Longitudinal monitoring**: Bias can emerge over time as usage patterns change
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