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Designs review workflows, checklists, and processes to detect and mitigate bias in AI outputs, including types of bias, detection methods, and mitigation strategies.
How this skill is triggered — by the user, by Claude, or both
Slash command
/ai-alignment-reasoning:bias-detection-designThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
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.
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.
Bias detection is a team practice, not a one-time audit:
Finding bias is step one. Addressing it requires:
npx claudepluginhub owl-listener/ai-design-skills --plugin ai-alignment-reasoningAudits algorithms, models, ranking systems, and automated decisions for discriminatory patterns and unfair outcomes. Use before deploying any system that makes decisions about people.
Conducts a structured ethical review of AI/ML features, models, or products covering fairness, transparency, privacy, safety, accountability, and societal impact with risk scoring and mitigations.
Measures ML model performance across demographic groups to detect discriminatory outcomes. Required for regulatory compliance (EU AI Act, CFPB, EEOC) and ethical AI deployment.