Explainer Model

Explainer models aim to make the decisions of complex machine learning models more transparent and understandable, addressing the "black box" problem. Current research focuses on improving the fidelity and plausibility of explanations, exploring techniques like extractive rationalization, counterfactual analysis, and rule-based approaches, often incorporating natural language processing for generating human-readable explanations. These advancements are crucial for building trust in AI systems across various domains, from autonomous vehicles to medical diagnosis, and for facilitating better human-AI collaboration and debugging.