Explanation Output

Explanation output in machine learning focuses on making the predictions of complex models, such as deep neural networks and tree ensembles, more understandable and trustworthy. Current research emphasizes developing methods to extract interpretable rules from models, generate natural language explanations for predictions, and provide contrastive explanations that highlight the evidence supporting a model's decision. This work is crucial for building trust in AI systems, improving model debugging and refinement, and enabling more effective human-computer interaction in diverse applications ranging from grammatical error correction to image classification and electricity forecasting.