Fairness is increasingly recognized as a critical component of machine learning systems.
However, it is the underlying data on which these systems are trained that often reflect …

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 …

The increasing use of data-driven decision support systems in industry and governments is
accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of …

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 …

This accessible, alphabetical guide provides concise insights into a variety of digital
research methods, incorporating introductory knowledge with practical application and …

Dirty data and deceptive design practices can undermine, invert, or invalidate the purported
messages of charts and graphs. These failures can arise silently: a conclusion derived from …

Machine learning models risk encoding unfairness on the part of their developers or data
sources. However, assessing fairness is challenging as analysts might misidentify sources …

Data provenance has evolved from a niche topic to a mainstream area of research in
databases and other research communities. This article gives a comprehensive introduction …

In this work, we provide some insights and develop some ideas, with few technical details,
about the role of explanations in Data Quality in the context of data-based machine learning …

Fairness is increasingly recognized as a critical component of machine learning systems.
However, it is the underlying data on which these systems are trained that often reflect …