Individual skill
s4h-ethics-bias-check
Evaluate an algorithm, model, ranking system, recommendation engine, or automated decision process for discriminatory patterns and unfair outcomes. Use before deploying any system that makes decisions about people or ranks/filters/scores people. TRIGGERS: 'bias check', 'check this algorithm for bias', 'is this fair', 'fairness audit', any ML model, ranking function, scoring system, content recommendation, or automated decision that differentiates between people.
skills/s4h-ethics-bias-check/SKILL.md
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- Yes
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- Yes
- Observed behavior
- Not checked yet
- Last checked
- Nov 5, 2026
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- Repository stars (not skill ratings)
- 240
- Repository last updated
- Jul 15, 2026
- License
- MIT
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- claude-code Repository topic
- claude-code-plugin Repository topic
- cognitive-frameworks Repository topic
- creativity Repository topic
- decision-making Repository topic
- ethics Repository topic
- mental-models Repository topic
- problem-solving Repository topic
- reasoning Repository topic
- systems-thinking Repository topic
- operations Derived category
- writing Derived category
- security Derived category
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# Ethics Bias Check
Algorithms that treat everyone the same can still discriminate. A ranking that optimises for engagement may systematically deprioritise certain groups. A model trained on historical data may encode historical injustice. A feature that works well on average may fail badly for users who aren't the implicit default.
This check surfaces those patterns before they ship.
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## Your Process
**Step 1: Define the system**
What is the algorithm, model, or automated decision? What is its input? What is its output? Who does it make decisions *about*? What happens to people based on its output?
**Framing check:** Confirm the specific system before continuing. State what you've identified — the actual system being audited, its outputs, and the population it affects — in one sentence, then use `AskUserQuestion`: