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 …
user the predictions of a trained decision model by indicating the modifications to be made …
GLOBE-CE: A translation based approach for global counterfactual explanations
Counterfactual explanations have been widely studied in explainability, with a range of
application dependent methods prominent in fairness, recourse and model understanding …
application dependent methods prominent in fairness, recourse and model understanding …
Bayesian hierarchical models for counterfactual estimation
N Raman, D Magazzeni… - … Conference on Artificial …, 2023 - proceedings.mlr.press
Counterfactual explanations utilize feature perturbations to analyze the outcome of an
original decision and recommend an actionable recourse. We argue that it is beneficial to …
original decision and recommend an actionable recourse. We argue that it is beneficial to …
Global counterfactual explanations: Investigations, implementations and improvements
Counterfactual explanations have been widely studied in explainability, with a range of
application dependent methods emerging in fairness, recourse and model understanding …
application dependent methods emerging in fairness, recourse and model understanding …
[HTML][HTML] Counterfactual explanations for remaining useful life estimation within a Bayesian framework
Abstract Machine learning has contributed to the advancement of maintenance in many
industries, including aviation. In recent years, many neural network models have been …
industries, including aviation. In recent years, many neural network models have been …
X-Fake: Juggling Utility Evaluation and Explanation of Simulated SAR Images
SAR image simulation has attracted much attention due to its great potential to supplement
the scarce training data for deep learning algorithms. Consequently, evaluating the quality of …
the scarce training data for deep learning algorithms. Consequently, evaluating the quality of …
Optimization-Based Uncertainty Attribution Via Learning Informative Perturbations
Uncertainty attribution (UA) aims to identify key contributors to predictive uncertainty in deep
learning models. To improve the faithfulness of existing UA methods, we formulate UA as an …
learning models. To improve the faithfulness of existing UA methods, we formulate UA as an …