AI Bias: Detection and Mitigation Strategies

Understanding AI Bias

AI systems can reflect and amplify biases from training data, design choices, and deployment contexts.

Types of Bias

Data Bias

Historical bias

Representation bias

Measurement bias

Sampling bias

Algorithmic Bias

Aggregation bias

Learning bias

Evaluation bias

Deployment Bias

Population shift

Context mismatch

Detection Methods

Statistical Analysis

Disparate impact ratios

Group-level metrics

Individual fairness measures

Fairness Metrics

Demographic parity

Equalized odds

Calibration

Counterfactual fairness

Audit Approaches

Slice analysis

Stress testing

External audits

Mitigation Strategies

Pre-processing

Data augmentation

Resampling

Feature selection

In-processing

Fairness constraints

Adversarial training

Fair representation learning

Post-processing

Threshold adjustment

Calibration

Outcome equalization

Best Practices

Define fairness objectives early

Analyze training data

Test across subgroups

Implement monitoring

Establish feedback mechanisms

Tools and Frameworks

Fairlearn

AIF360

What-If Tool

SHAP/LIME

Organizational Actions

Diverse teams

Stakeholder input

Regular audits

Clear accountability

Conclusion

Addressing AI bias requires systematic attention throughout the ML lifecycle.