Ensuring AI ethics involves transparent model development, rigorous testing for bias, and incorporating diverse data sources. Explainability is crucial—techniques like interpretable machine learning (e.g., SHAP or LIME) can help make decisions understandable. In healthcare, strict regulatory standards (e.g., HIPAA) and oversight from domain experts ensure AI systems are both effective and accountable, while constant audits help avoid harmful biases or unintended outcomes.