Build responsible AI systems.

What is AI Bias?

When AI makes unfair decisions based on protected attributes.

**Problem**: AI learns from biased data!

Types of Bias

**Data bias**: Training data not representative **Algorithm bias**: Model design favors certain groups **Evaluation bias**: Test data doesn't reflect real world **Deployment bias**: AI used inappropriately

Detecting Bias

```python # Install # pip install fairlearn

from fairlearn.metrics import MetricFrame from sklearn.metrics import accuracy_score import pandas as pd

Assume you have predictions and sensitive features y_true = [0, 1, 1, 0, 1, 0] y_pred = [0, 1, 0, 0, 1, 1] gender = ['M', 'F', 'F', 'M', 'F', 'M']

Calculate metrics by group metric_frame = MetricFrame( metrics=accuracy_score, y_true=y_true, y_pred=y_pred, sensitive_features=gender )

print("Overall accuracy:", metric_frame.overall) print("By group:") print(metric_frame.by_group) ```

Fairness Metrics

```python from fairlearn.metrics import ( demographic_parity_difference, equalized_odds_difference )

Demographic parity dp = demographic_parity_difference( y_true, y_pred, sensitive_features=gender ) print(f"Demographic parity difference: {dp:.3f}")

Equalized odds eo = equalized_odds_difference( y_true, y_pred, sensitive_features=gender ) print(f"Equalized odds difference: {eo:.3f}") ```

Mitigating Bias

### 1. Better Data Collection ```python # Check data distribution df = pd.DataFrame({'gender': gender, 'outcome': y_true}) print(df.groupby('gender')['outcome'].value_counts())

Balance dataset from sklearn.utils import resample

Oversample minority group minority = df[df['gender'] == 'F'] majority = df[df['gender'] == 'M']

minority_upsampled = resample( minority, replace=True, n_samples=len(majority), random_state=42 )

balanced_df = pd.concat([majority, minority_upsampled]) ```

### 2. Fair Model Training ```python from fairlearn.reductions import ExponentiatedGradient, DemographicParity from sklearn.linear_model import LogisticRegression

Fair classifier model = LogisticRegression() constraint = DemographicParity()

mitigator = ExponentiatedGradient(model, constraint) mitigator.fit(X_train, y_train, sensitive_features=sensitive_train)

Predict y_pred = mitigator.predict(X_test) ```

### 3. Post-processing ```python from fairlearn.postprocessing import ThresholdOptimizer

Optimize thresholds per group optimizer = ThresholdOptimizer( estimator=model, constraints="demographic_parity" )

optimizer.fit(X_train, y_train, sensitive_features=sensitive_train) y_pred_fair = optimizer.predict(X_test, sensitive_features=sensitive_test) ```

Explainability

Make AI decisions transparent:

```python # Install # pip install shap

import shap

Explain predictions explainer = shap.Explainer(model, X_train) shap_values = explainer(X_test)

Visualize shap.plots.waterfall(shap_values[0]) shap.plots.beeswarm(shap_values) ```

Ethical Guidelines

**Transparency**: Explain how AI makes decisions **Fairness**: Treat all groups equally **Privacy**: Protect user data **Accountability**: Take responsibility for AI actions **Safety**: Ensure AI doesn't cause harm

Fairness Trade-offs

**Accuracy vs Fairness**: More fair model may be less accurate **Group fairness vs Individual fairness**: Can't always satisfy both **Different fairness definitions**: May conflict with each other

Best Practices

1. **Diverse teams**: Include different perspectives 2. **Regular audits**: Check for bias continuously 3. **User feedback**: Listen to affected communities 4. **Documentation**: Record decisions and trade-offs 5. **Testing**: Test on diverse populations

Real-world Examples

**Hiring**: Don't discriminate by gender/race **Lending**: Fair loan approval **Criminal justice**: Unbiased risk assessment **Healthcare**: Equal treatment recommendations

Remember

- All AI systems have bias - minimize it! - Test fairness on multiple groups - Balance fairness with accuracy - Be transparent about limitations - Ethics is ongoing, not one-time