Fairlearn Bias Detector

Name: Fairlearn Bias Detector
Author: a5c-ai

By a5c-ai· a5c-ai/babysitter· 0

Detect and mitigate bias in machine learning models with fairness metrics and compliance reports.

Installation

1
Make sure Claude is on your device and in your terminal.
Skills load from ~/.claude/skills/ when Claude Code starts up — so you need it on your machine first. If you don't have it yet, install it once with the command below, then run claude in any terminal to verify.
One-time setup
```
npm i -g @anthropic-ai/claude-code
```
Already have it? Skip ahead.

Paste into Claude Code or into your terminal.

Install

git clone ht•••••••••••••••••••••••••••••••••••••• ••••••••••••••••••••••• •• ••••• •• ••••••••••••••••••••••••••••••••••••••••••••••• •• •• •• •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• ••••••••••••••••••••••••••••••••••••••••••••••••

This copies the whole skill folder into ~/.claude/skills/fairlearn-bias-detector-a5c-ai/ — the SKILL.md plus any scripts, reference docs, or templates the skill ships with. Safe default: works for every skill.

Faster alternative (instruction-only skills)

Skips the clone and grabs only the SKILL.md file. Don't use this if the skill ships Python scripts, reference markdowns, or asset templates — they won't be downloaded and the skill will fail when it tries to load them.

Quick install (SKILL.md only)

mkdir -p ~/.•••••••••••••••••••••••••••••••••••••••••••• •• •••• ••••• •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• •• ••••••••••••••••••••••••••••••••••••••••••••••••••••••••

Restart Claude Code.
Quit and reopen Claude Code (or any other agent that loads from ~/.claude/skills/). New skills are picked up on startup.
Just ask Claude.
Skills auto-activate when your request matches the skill's description — no slash command needed. Trigger phrases live in the skill's own frontmatter; you can read them in the “What this skill does” section above.

Prefer to read the source first? Open on GitHub.

When Claude uses it

Fairness assessment skill using Fairlearn for bias detection, mitigation, and compliance reporting.

What this skill does

fairlearn-bias-detector

Overview

Fairness assessment skill using Fairlearn for bias detection, mitigation, and compliance reporting in ML models.

Capabilities

Demographic parity assessment
Equalized odds evaluation
Disparity metrics calculation
Bias mitigation algorithms (preprocessing, in-processing, post-processing)
Fairness constraint optimization
Compliance documentation generation
Intersectional fairness analysis
Threshold optimization for fairness

Target Processes

Model Evaluation and Validation Framework
Model Interpretability and Explainability Analysis
A/B Testing Framework for ML Models

Tools and Libraries

Fairlearn
scikit-learn
pandas

Input Schema

{
  "type": "object",
  "required": ["modelPath", "dataPath", "sensitiveFeatures"],
  "properties": {
    "modelPath": {
      "type": "string",
      "description": "Path to the trained model"
    },
    "dataPath": {
      "type": "string",
      "description": "Path to evaluation data"
    },
    "sensitiveFeatures": {
      "type": "array",
      "items": { "type": "string" },
      "description": "Column names of sensitive attributes"
    },
    "labelColumn": {
      "type": "string",
      "description": "Name of the target/label column"
    },
    "assessmentConfig": {
      "type": "object",
      "properties": {
        "metrics": {
          "type": "array",
          "items": {
            "type": "string",
            "enum": ["demographic_parity", "equalized_odds", "true_positive_rate", "false_positive_rate", "accuracy"]
          }
        },
        "threshold": { "type": "number" }
      }
    },
    "mitigationConfig": {
      "type": "object",
      "properties": {
        "method": {
          "type": "string",
          "enum": ["threshold_optimizer", "exponentiated_gradient", "grid_search", "reductions"]
        },
        "constraint": { "type": "string" },
        "gridSize": { "type": "integer" }
      }
    }
  }
}

Output Schema

{
  "type": "object",
  "required": ["status", "assessment"],
  "properties": {
    "status": {
      "type": "string",
      "enum": ["success", "error"]
    },
    "assessment": {
      "type": "object",
      "properties": {
        "overallMetrics": { "type": "object" },
        "groupMetrics": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "group": { "type": "string" },
              "count": { "type": "integer" },
              "metrics": { "type": "object" }
            }
          }
        },
        "disparityMetrics": {
          "type": "object",
          "properties": {
            "demographicParityDiff": { "type": "number" },
            "equalizedOddsDiff": { "type": "number" }
          }
        },
        "fairnessScore": { "type": "number" }
      }
    },
    "mitigation": {
      "type": "object",
      "properties": {
        "method": { "type": "string" },
        "improvedModel": { "type": "string" },
        "beforeMetrics": { "type": "object" },
        "afterMetrics": { "type": "object" }
      }
    },
    "complianceReport": {
      "type": "string",
      "description": "Path to generated compliance report"
    }
  }
}

Usage Example

{
  kind: 'skill',
  title: 'Assess model fairness',
  skill: {
    name: 'fairlearn-bias-detector',
    context: {
      modelPath: 'models/loan_model.pkl',
      dataPath: 'data/test.csv',
      sensitiveFeatures: ['gender', 'race'],
      labelColumn: 'approved',
      assessmentConfig: {
        metrics: ['demographic_parity', 'equalized_odds'],
        threshold: 0.8
      },
      mitigationConfig: {
        method: 'threshold_optimizer',
        constraint: 'demographic_parity'
      }
    }
  }
}

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