Counterfactual explanations and how to find them: literature review and benchmarking
R Guidotti - Data Mining and Knowledge Discovery, 2024 - Springer
Interpretable machine learning aims at unveiling the reasons behind predictions returned by
uninterpretable classifiers. One of the most valuable types of explanation consists of …
uninterpretable classifiers. One of the most valuable types of explanation consists of …
Benchmarking and survey of explanation methods for black box models
The rise of sophisticated black-box machine learning models in Artificial Intelligence
systems has prompted the need for explanation methods that reveal how these models work …
systems has prompted the need for explanation methods that reveal how these models work …
On the computation of counterfactual explanations--A survey
Due to the increasing use of machine learning in practice it becomes more and more
important to be able to explain the prediction and behavior of machine learning models. An …
important to be able to explain the prediction and behavior of machine learning models. An …
[HTML][HTML] Model-based explanations of concept drift
Abstract Concept drift refers to the phenomenon that the distribution generating the
observed data changes over time. If drift is present, machine learning models can become …
observed data changes over time. If drift is present, machine learning models can become …
Convex density constraints for computing plausible counterfactual explanations
The increasing deployment of machine learning as well as legal regulations such as EU's
GDPR cause a need for user-friendly explanations of decisions proposed by machine …
GDPR cause a need for user-friendly explanations of decisions proposed by machine …
Keep your friends close and your counterfactuals closer: Improved learning from closest rather than plausible counterfactual explanations in an abstract setting
Counterfactual explanations (CFEs) highlight changes to a model's input that alter its
prediction in a particular way. s have gained considerable traction as a psychologically …
prediction in a particular way. s have gained considerable traction as a psychologically …
Let's go to the Alien Zoo: Introducing an experimental framework to study usability of counterfactual explanations for machine learning
Introduction To foster usefulness and accountability of machine learning (ML), it is essential
to explain a model's decisions in addition to evaluating its performance. Accordingly, the …
to explain a model's decisions in addition to evaluating its performance. Accordingly, the …
Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers
The increasing use of machine learning in practice and legal regulations like EU's GDPR
cause the necessity to be able to explain the prediction and behavior of machine learning …
cause the necessity to be able to explain the prediction and behavior of machine learning …
Efficient computation of counterfactual explanations of LVQ models
The increasing use of machine learning in practice and legal regulations like EU's GDPR
cause the necessity to be able to explain the prediction and behavior of machine learning …
cause the necessity to be able to explain the prediction and behavior of machine learning …
Ensemble of counterfactual explainers
R Guidotti, S Ruggieri - … Science: 24th International Conference, DS 2021 …, 2021 - Springer
Abstract In eXplainable Artificial Intelligence (XAI), several counterfactual explainers have
been proposed, each focusing on some desirable properties of counterfactual instances …
been proposed, each focusing on some desirable properties of counterfactual instances …