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name: ds-model-fairness compatibility: opencode completeness: 95 content-types:

• code
• guidance
• do-dont
• examples description: '"Evaluates and mitigates fairness issues including bias detection, fairness metrics, and debiasing strategies in machine learning"' license: MIT maturity: stable metadata: domain: coding output-format: code related-skills: ds-explainability, ds-model-interpretation, ds-model-robustness ds-privacy-ml ds-privacy-ml role: implementation scope: implementation triggers: model fairness, fairness metrics, bias detection, debiasing, fair machine learning, how do I check bias archetypes:
  • tactical
  • generation anti_triggers:
  • brainstorming
  • vague ideation
  • code golf
  • over-engineering response_profile: verbosity: low directive_strength: high abstraction_level: operational version: "1.0.0"

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Model Fairness

Comprehensive guide to model fairness in machine learning and data science workflows.

When to Use This Skill

• Solving real-world reproducibility & responsible ai problems
• Building machine learning pipelines with model fairness
• Implementing best practices for model fairness
• Optimizing model performance using model fairness techniques
• Learning industry-standard approaches to model fairness

When NOT to Use This Skill

• When using pre-built libraries without understanding underlying concepts
• For toy problems that don't require model fairness rigor
• When domain expertise in specific problem requires different approach
• If your problem doesn't require the complexity this skill provides

Purpose and Key Concepts

Model Fairness is a critical component of the machine learning workflow. This skill covers:

1. Theoretical foundations — Mathematical principles and statistical concepts
2. Practical implementation — Working code examples and patterns
3. Common pitfalls — Mistakes to avoid and how to recover from them
4. Best practices — Industry-standard approaches and optimization techniques

Core Workflow

1. Understand the problem — Clearly define what you're solving for
2. Select approach — Choose the right technique for your data and constraints
3. Implement solution — Write clean, tested code following best practices
4. Validate results — Verify your implementation with tests and validation
5. Optimize performance — Improve efficiency and accuracy incrementally

Implementation Patterns

Pattern 1: Basic Model Fairness

import pandas as pd
import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Generate synthetic dataset with a sensitive attribute
X, y = make_classification(n_samples=1000, n_features=5, n_informative=3, random_state=42)
sensitive_attr = np.random.randint(0, 2, size=1000)
df = pd.DataFrame(X, columns=[f'feat_{i}' for i in range(5)])
df['target'] = y
df['sensitive'] = sensitive_attr

# Split data
train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)

# Train baseline model
model = LogisticRegression(random_state=42)
model.fit(train_df.drop(columns=['target', 'sensitive']), train_df['target'])

# Predictions
y_pred = model.predict(test_df.drop(columns=['target', 'sensitive']))

# Calculate Demographic Parity Difference
def calc_demographic_parity_diff(y_true, y_pred, sensitive):
    groups = np.unique(sensitive)
    rates = [np.mean(y_pred[sensitive == g]) for g in groups]
    return max(rates) - min(rates)

dp_diff = calc_demographic_parity_diff(test_df['target'], y_pred, test_df['sensitive'])
print(f"Demographic Parity Difference: {dp_diff:.4f}")

Pattern 2: Production-Ready Model Fairness

import logging
import pandas as pd
import numpy as np
from typing import Dict, Any, List
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_classification

logger = logging.getLogger(__name__)

class ModelFairnessAuditor:
    """Production-grade fairness auditing and debiasing class"""
    
    def __init__(self, sensitive_features: str, target_col: str, threshold: float = 0.5):
        self.sensitive_features = sensitive_features
        self.target_col = target_col
        self.threshold = threshold
        self.model = None
        self.results: Dict[str, Any] = {}

    def _validate_input(self, df: pd.DataFrame) -> None:
        if df is None or df.empty:
            raise ValueError("Input DataFrame cannot be None or empty")
        required_cols = [self.sensitive_features, self.target_col]
        missing = [col for col in required_cols if col not in df.columns]
        if missing:
            raise ValueError(f"Missing required columns: {missing}")

    def _calculate_metrics(self, y_true: np.ndarray, y_pred: np.ndarray, sensitive: np.ndarray) -> Dict[str, float]:
        groups = np.unique(sensitive)
        if len(groups) < 2:
            return {"error": "Need at least two groups for fairness metrics"}
        
        group_rates = {g: np.mean(y_pred[sensitive == g]) for g in groups}
        dp_diff = max(group_rates.values()) - min(group_rates.values())
        
        tp_rates = {g: np.mean(y_pred[sensitive == g] & y_true == 1) / max(np.sum(y_true == 1), 1) for g in groups}
        eod = max(tp_rates.values()) - min(tp_rates.values())
        
        return {"demographic_parity_diff": dp_diff, "equal_opportunity_diff": eod}

    def execute(self, data: pd.DataFrame) -> Dict[str, Any]:
        self._validate_input(data)
        try:
            X = data.drop(columns=[self.target_col, self.sensitive_features])
            y = data[self.target_col]
            sensitive = data[self.sensitive_features]
            
            X_train, X_test, y_train, y_test, s_train, s_test = train_test_split(
                X, y, sensitive, test_size=0.2, random_state=42
            )
            
            self.model = RandomForestClassifier(n_estimators=50, random_state=42)
            self.model.fit(X_train, y_train)
            y_pred = self.model.predict(X_test)
            
            metrics = self._calculate_metrics(y_test.values, y_pred, s_test.values)
            self.results = {
                "status": "success"
                "metrics": metrics
                "predictions": y_pred.tolist()
                "model_type": "RandomForestClassifier"
            }
            logger.info(f"Fairness audit complete. DP Diff: {metrics['demographic_parity_diff']:.4f}")
            return self.results
        except Exception as e:
            logger.error(f"Audit failed: {str(e)}")
            return {"status": "error", "message": str(e)}

Best Practices

• ✅ Always validate your implementation on test data
• ✅ Document your assumptions and methodology
• ✅ Use version control for reproducibility
• ✅ Monitor performance metrics in production
• ✅ Periodically review and update your approach
• ✅ Test with edge cases and outliers
• ✅ Log all significant operations for debugging

Common Pitfalls

| Pitfall | Problem | Solution | |

---

---

Constraints

MUST DO

• Validate all data preprocessing steps are fit-only on training data, never on validation or test sets
• Implement reproducible pipelines with fixed random seeds and deterministic operations where possible
• Report model performance with confidence intervals via bootstrapping or cross-validation across multiple runs
• Log all experiments with parameters, metrics, and artifacts using MLflow or equivalent tracking system

MUST NOT DO

• Do not evaluate a model on the same data used for training — always hold out a proper test set
• Avoid overfitting to the validation set by limiting hyperparameter search iterations
• Never use features that can only be computed at inference time (look-ahead bias)
• Do not report single-run accuracy without statistical significance testing or error bars

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Fairlearn Documentation
• Fairness in Machine Learning — Wikipedia
• AI Fairness 360 (IBM)
• Google What-If Tool
• Explainable AI for ML — Microsoft Azure