AI Ethics and Bias

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

# 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

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

# 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

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

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:

# 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

Diverse teams: Include different perspectives
Regular audits: Check for bias continuously
User feedback: Listen to affected communities
Documentation: Record decisions and trade-offs
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