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Found 9 papers, 4 papers with code
A Robust Autoencoder Ensemble-Based Approach for Anomaly Detection in Text
In this work, a robust autoencoder ensemble-based approach designed to address anomaly detection in text corpora is introduced.
Dynamic Interpretability for Model Comparison via Decision Rules
Explainable AI (XAI) methods have mostly been built to investigate and shed light on single machine learning models and are not designed to capture and explain differences between multiple models effectively.
Achieving Diversity in Counterfactual Explanations: a Review and Discussion
In the field of Explainable Artificial Intelligence (XAI), counterfactual examples explain to a user the predictions of a trained decision model by indicating the modifications to be made to the instance so as to change its associated prediction.
Integrating Prior Knowledge in Post-hoc Explanations
In the field of eXplainable Artificial Intelligence (XAI), post-hoc interpretability methods aim at explaining to a user the predictions of a trained decision model.
The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations
Post-hoc interpretability approaches have been proven to be powerful tools to generate explanations for the predictions made by a trained black-box model.
Issues with post-hoc counterfactual explanations: a discussion
Counterfactual post-hoc interpretability approaches have been proven to be useful tools to generate explanations for the predictions of a trained blackbox classifier.
Detecting Potential Local Adversarial Examples for Human-Interpretable Defense
Machine learning models are increasingly used in the industry to make decisions such as credit insurance approval.
Defining Locality for Surrogates in Post-hoc Interpretablity
Local surrogate models, to approximate the local decision boundary of a black-box classifier, constitute one approach to generate explanations for the rationale behind an individual prediction made by the back-box.
Inverse Classification for Comparison-based Interpretability in Machine Learning
In the context of post-hoc interpretability, this paper addresses the task of explaining the prediction of a classifier, considering the case where no information is available, neither on the classifier itself, nor on the processed data (neither the training nor the test data).
