Explanations have gained an increasing level of interest in the AI and Machine Learning
(ML) communities in order to improve model transparency and allow users to form a mental …

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 …

Insights from cognitive science about how people understand explanations can be
instructive for the development of robust, user-centred explanations in eXplainable Artificial …

Counterfactual explanations (CEs) are a powerful means for understanding how decisions
made by algorithms can be changed. Researchers have proposed a number of desiderata …

Large language model (LLM)–powered services are gaining popularity in various
applications due to their exceptional performance in many tasks, such as sentiment analysis …

Algorithmic recourse discloses the internal procedures of a black-box decision process
where decisions have significant consequences by providing recommendations to empower …

Counterfactual explanations (CEs) play an important role in detecting bias and improving
the explainability of data-driven classification models. A CE is a minimal perturbed data …

Algorithmic systems are often called upon to assist in high-stakes decision making. In light of
this, algorithmic recourse, the principle wherein individuals should be able to take action …

Counterfactual Analysis has shown to be a powerful tool in the burgeoning field of
Explainable Artificial Intelligence. In Supervised Classification, this means associating with …

Different users of machine learning methods require different explanations, depending on
their goals. To make machine learning accountable to society, one important goal is to get …