ReddRadar
Agent skill
fairlearn-bias-detector
Fairness assessment skill using Fairlearn for bias detection, mitigation, and compliance reporting.
Install this agent skill to your Project
npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/data-science-ml/skills/fairlearn-bias-detector
SKILL.md
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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