Explainable Ensemble

Explainable ensembles aim to improve the transparency and interpretability of ensemble machine learning models, which often achieve high predictive accuracy but lack insight into their decision-making processes. Current research focuses on developing methods to explain ensembles of tree-based models (like random forests and XGBoost), as well as deep learning models, using techniques such as rule extraction, saliency maps, and counterfactual explanations. This work is significant because it addresses the critical need for trust and understanding in complex AI systems, particularly in high-stakes applications like healthcare and security, where understanding model predictions is crucial for responsible deployment. The development of consistent and easily interpretable explanations for ensemble predictions is a key area of ongoing investigation.