Post-hoc explanation methods for machine learning models have been widely used to support decision-making. One of the popular methods is Counterfactual Explanation (CE), also known as Actionable Recourse, which provides a user with a perturbation vector of features that alters the prediction result. Given a perturbation vector, a user can interpret it as an "action" for obtaining one's desired decision result. In practice, however, showing only a perturbation vector is often insufficient for users to execute the action. The reason is that if there is an asymmetric interaction among features, such as causality, the total cost of the action is expected to depend on the order of changing features. Therefore, practical CE methods are required t...
Counterfactual explanation is an important Explainable AI technique to explain machine learning pred...
Counterfactual explanations focus on 'actionable knowledge' to help end-users understand how a Machi...
We consider counterfactual explanations, the problem of minimally adjusting features in a source inp...
Machine learning plays a role in many deployed decision systems, often in ways that are difficult or...
Counterfactual explanations describe how an outcome can be changed to a more desirable one. In XAI, ...
To increase the adoption of counterfactual explanations in practice, several criteria that these sho...
A method for counterfactual explanation of machine learning survival models is proposed. One of the ...
Counterfactual explanations focus on “actionable knowledge” to help end-users understand how a Machi...
Counterfactual explanations describe how an outcome can be changed to a more desirable one. In XAI, ...
Counterfactual explanations are viewed as an effective way to explain machine learning predictions. ...
The demand for explainable machine learning (ML) models has been growing rapidly in recent years. Am...
Abstract—Counterfactual explanations focus on “actionable knowledge” to help end-users understand ho...
International audienceRecent efforts have uncovered various methods for providing explanations that ...
Being able to provide counterfactual interventions - sequences of actions we would have had to take ...
Due to the increasing use of Machine Learning models in high stakes decision making settings, it has...
Counterfactual explanation is an important Explainable AI technique to explain machine learning pred...
Counterfactual explanations focus on 'actionable knowledge' to help end-users understand how a Machi...
We consider counterfactual explanations, the problem of minimally adjusting features in a source inp...
Machine learning plays a role in many deployed decision systems, often in ways that are difficult or...
Counterfactual explanations describe how an outcome can be changed to a more desirable one. In XAI, ...
To increase the adoption of counterfactual explanations in practice, several criteria that these sho...
A method for counterfactual explanation of machine learning survival models is proposed. One of the ...
Counterfactual explanations focus on “actionable knowledge” to help end-users understand how a Machi...
Counterfactual explanations describe how an outcome can be changed to a more desirable one. In XAI, ...
Counterfactual explanations are viewed as an effective way to explain machine learning predictions. ...
The demand for explainable machine learning (ML) models has been growing rapidly in recent years. Am...
Abstract—Counterfactual explanations focus on “actionable knowledge” to help end-users understand ho...
International audienceRecent efforts have uncovered various methods for providing explanations that ...
Being able to provide counterfactual interventions - sequences of actions we would have had to take ...
Due to the increasing use of Machine Learning models in high stakes decision making settings, it has...
Counterfactual explanation is an important Explainable AI technique to explain machine learning pred...
Counterfactual explanations focus on 'actionable knowledge' to help end-users understand how a Machi...
We consider counterfactual explanations, the problem of minimally adjusting features in a source inp...