Example Concept: In healthcare, evaluating AI involves rigorous testing for bias and fairness, ensuring models do not disproportionately affect any demographic group. This includes using fairness metrics and bias detection tools to analyze model predictions. Additionally, transparency techniques such as model cards and explainability methods like SHAP or LIME are employed to provide insights into model decision-making processes, ensuring stakeholders understand how decisions are made.