The use of artificial intelligence (AI) is becoming more prevalent across industries such as
healthcare, finance, and transportation. Artificial intelligence is based on the analysis of …

In this paper, we review algorithmic bias in education, discussing the causes of that bias and
reviewing the empirical literature on the specific ways that algorithmic bias is known to have …

Abstract Trustworthy Artificial Intelligence (AI) is based on seven technical requirements
sustained over three main pillars that should be met throughout the system's entire life cycle …

Recent advancements in Large Language Models (LLMs) suggest imminent commercial
applications of such AI systems where they will serve as gateways to interact with …

Fueled by ever-growing amounts of (digital) data and advances in artificial intelligence,
decision-making in contemporary societies is increasingly delegated to automated …

In recent years, machine learning (ML) has become a key enabling technology for the
sciences and industry. Especially through improvements in methodology, the availability of …

Much of the previous machine learning (ML) fairness literature assumes that protected
features such as race and sex are present in the dataset, and relies upon them to mitigate …

In the past few years, there has been much work on incorporating fairness requirements into
algorithmic rankers, with contributions coming from the data management, algorithms …

This tutorial extensively covers the definitions, nuances, challenges, and requirements for
the design of interpretable and explainable machine learning models and systems in …

Advances in artificial intelligence, sensors and big data management have far-reaching
societal impacts. As these systems augment our everyday lives, it becomes increasing-ly …