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Found 3 papers, 2 papers with code
Contrastive Explanations of Text Classifiers as a Service
The recent growth of black-box machine-learning methods in data analysis has increased the demand for explanation methods and tools to understand their behaviour and assist human-ML model cooperation.
Model-contrastive explanations through symbolic reasoning
Explaining how two machine learning classification models differ in their behaviour is gaining significance in eXplainable AI, given the increasing diffusion of learning-based decision support systems.
Counterfactual Explanation
Explainable artificial intelligence
ContrXT: Generating Contrastive Explanations from any Text Classifier
The need for explanations of ML systems is growing as new models outperform their predecessors while becoming more complex and less comprehensible for their end-users.
