Counterfactual explanations were first introduced as a human-centric way to understand
model behavior. While validity remains core to the counterfactual explanation definition …

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 …

Machine learning is increasingly applied in high-stakes decision making that directly affect
people's lives, and this leads to an increased demand for systems to explain their decisions …

As an essential post-hoc explanatory method, counterfactual explanation enables people to
understand and react to machine learning models. Works on counterfactual explanation …

Counterfactual explanations have emerged as an important tool to understand, debug, and
audit complex machine learning models. To offer global counterfactual explainability, state …

This work presents CounterNet, a novel end-to-end learning framework which integrates
Machine Learning (ML) model training and the generation of corresponding counterfactual …

Machine learning plays a role in many deployed decision systems, often in ways that are
difficult or impossible to understand by human stakeholders. Explaining, in a human …

Counterfactual explanations (CFEs) exemplify how to minimally modify a feature vector to
achieve a different prediction for an instance. CFEs can enhance informational fairness and …

Predictive systems based on high-dimensional behavioral and textual data have serious
comprehensibility and transparency issues: linear models require investigating thousands of …

A pressing issue in the adoption of AI models is the increasing demand for more human-
centric explanations of their predictions. To advance towards more human-centric …