Techniques for Ensuring Ethical AI: Bias Mitigation, Explainability, and Robustness

Intermediate

Practical techniques to embed ethics into AI development include:

  • Bias Mitigation: Methods such as re-sampling, fairness constraints, and adversarial training reduce biased outcomes.
  • Explainability: Tools like LIME, SHAP, and attention mechanisms help interpret model decisions.
  • Robustness and Security: Techniques like adversarial training, differential privacy, and model validation improve resilience.

Code Example: Bias detection using Python and scikit-learn:

from sklearn.metrics import classification_report
# Assume y_true and y_pred are actual and predicted labels
print(classification_report(y_true, y_pred, target_names=['Group A', 'Group B']))

This assists in identifying disparities between groups to address bias.