Machine learning is increasingly used to inform decision making in sensitive situations
where decisions have consequential effects on individuals' lives. In these settings, in …

Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging
and trusting statistical and deep learning models, as well as interpreting their predictions …

Understanding black box models has become paramount as systems based on opaque
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …

Abstract Nowadays Artificial Intelligence (AI) has become a fundamental component of
healthcare applications, both clinical and remote, but the best performing AI systems are …

Abstract Interpretable Machine Learning (IML) has rapidly advanced in recent years, offering
new opportunities to improve our understanding of the complex Earth system. IML goes …

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 …

Abstract Machine learning and deep learning have become the most useful and powerful
tools in the last years to mine information from large datasets. Despite the successful …

Counterfactual explanations inform ways to achieve a desired outcome from a machine
learning model. However, such explanations are not robust to certain real-world changes in …

Counterfactual explanations have been widely studied in explainability, with a range of
application dependent methods prominent in fairness, recourse and model understanding …

Intelligent applications supported by Machine Learning have achieved remarkable
performance rates for a wide range of tasks in many domains. However, understanding why …