Causal Interpretability for Machine Learning - Problems, Methods and Evaluation
Causal Interpretability for Machine Learning - Problems, Methods and Evaluation
Machine learning models have had discernible achievements in a myriad of applications. However, most of these models are black-boxes, and it is obscure how the decisions are made by them. This makes the models unreliable and untrustworthy. To provide insights into the decision making processes of these models, a variety …