SHAP explanations are a popular feature-attribution mechanism for explainable AI. They
use game-theoretic notions to measure the influence of individual features on the prediction …

There has been a recent resurgence of interest in explainable artificial intelligence (XAI) that
aims to reduce the opaqueness of AI-based decision-making systems, allowing humans to …

A wide variety of fairness metrics and eXplainable Artificial Intelligence (XAI) approaches
have been proposed in the literature to identify bias in machine learning models that are …

Access to high-quality data repositories and benchmarks have been instrumental in
advancing the state of the art in many experimental research domains. While advanced …

In light of the growing popularity of Exploratory Data Analysis (EDA), understanding the
underlying causes of the knowledge acquired by EDA is crucial. However, it remains under …

Scores based on Shapley values are widely used for providing explanations to classification
results over machine learning models. A prime example of this is the inuential SHAP-score …

Attribution scores can be applied in data management to quantify the contribution of
individual items to conclusions from the data, as part of the explanation of what led to these …

Humans reason about the world around them by seeking to understand why and how
something occurs. The same principle extends to the technology that so many of human …

In this expository article we highlight the relevance of explanations for artificial intelligence,
in general, and for the newer developments in explainable AI, referring to origins and …

We propose answer-set programs that specify and compute counterfactual interventions on
entities that are input on a classification model. In relation to the outcome of the model, the …