Entropy

Handling Bias and Ethical Considerations

Bias in Machine Learning can lead to unfair or discriminatory outcomes. Ethical considerations must be addressed by ensuring datasets are diverse, representative, and free from historical biases.

Techniques such as Fairness-aware Machine Learning, Explainable AI (XAI), and Model Interpretability help mitigate bias and build trustworthy AI systems. Compliance with regulations like GDPR and ethical AI frameworks is essential in sensitive applications such as healthcare and finance.