Intuitive Explanation

Intuitive explanation in artificial intelligence focuses on making the decision-making processes of complex models, such as large language models and deep neural networks, more transparent and understandable to humans. Current research emphasizes developing methods to visualize model reasoning, quantify feature importance, and align model outputs with human intuitions, often employing techniques like attention maps, Shapley values, and causal inference frameworks. This work is crucial for building trust in AI systems, improving their reliability in high-stakes applications (e.g., medical diagnosis), and facilitating more effective human-AI collaboration.