Explainable Artificial Intelligence

4 credits

Approaches to enhancing the interpretability and transparency of complex machine learning models, encompassing both inherently interpretable models and post hoc explanation methods. Explores a spectrum of techniques, ranging from feature attributions and their evaluation metrics to counterfactual explanations, concept-based explanations, instance explanations, and collaboration between humans and artificial intelligence.