Explainability Tool

Explainability tools aim to make the decision-making processes of complex machine learning models, particularly "black box" models like deep neural networks and large language models, more transparent and understandable. Current research focuses on developing methods to provide both local (instance-specific) and global explanations, employing techniques like counterfactual analysis, feature attribution, and interactive dialogue systems. These tools are crucial for building trust in AI systems, improving model debugging and fairness, and enabling responsible deployment across diverse applications, including healthcare, finance, and security.