Counterfactual instances are a powerful tool to obtain valuable insights into automated
decision processes, describing the necessary minimal changes in the input space to alter …

The demand for explainable machine learning (ML) models has been growing rapidly in
recent years. Amongst the methods proposed to associate ML model predictions with …

Counterfactual instances offer human-interpretable insight into the local behaviour of
machine learning models. We propose a general framework to generate sparse, in …

There is a broad consensus on the importance of deep learning models in tasks involving
complex data. Often, an adequate understanding of these models is required when focusing …

Generating counterfactuals to discover hypothetical predictive scenarios is the de facto
standard for explaining machine learning models and their predictions. However, building a …

To increase the adoption of counterfactual explanations in practice, several criteria that
these should adhere to have been put forward in the literature. We propose counterfactual …

Counterfactual explanation is an important Explainable AI technique to explain machine
learning predictions. Despite being studied actively, existing optimization-based methods …

This work presents CounterNet, a novel endto-end learning framework which integrates the
predictive model training and the counterfactual (CF) explanation into a single end-to-end …

Counterfactual examples (CFs) are one of the most popular methods for attaching post hoc
explanations to machine learning models. However, existing CF generation methods either …

As black-box models increasingly power high-stakes applications, a variety of data-driven
explanation methods have been introduced. Meanwhile, machine learning models are …