Concept Based Explanation

Concept-based explanation aims to make the decisions of complex machine learning models, particularly deep neural networks, more transparent and understandable by representing them in terms of high-level human-interpretable concepts. Current research focuses on developing methods for automatically discovering and utilizing these concepts, often employing techniques like disentangled representation learning, reinforcement learning, and generative models to create concept-based explanations, even with limited or no human annotation. This field is crucial for building trust in AI systems across various applications, from medical diagnosis to autonomous driving, by providing more insightful and reliable explanations than traditional methods.