Explainability Method

Explainability methods aim to make the decision-making processes of complex machine learning models, particularly deep neural networks and large language models, more transparent and understandable. Current research focuses on developing and evaluating methods that assess the faithfulness and plausibility of explanations, often using techniques like counterfactual generation, attribution methods (e.g., SHAP, LIME, Grad-CAM), and concept-based approaches. This work is crucial for building trust in AI systems across diverse applications, from medical diagnosis to autonomous vehicles, by providing insights into model behavior and identifying potential biases.