A new and unorthodox approach to deal with discriminatory bias in Artificial Intelligence is
needed. As it is explored in detail, the current literature is a dichotomy with studies …

In this paper we study the limitations of Machine Learning (ML) algorithms for predicting
juvenile recidivism. Particularly, we are interested in analyzing the trade-off between …

AI auditing is a rapidly growing field of research and practice. This review article, which
doubles as an editorial to Digital Society's topical collection on 'Auditing of AI', provides an …

Recommender systems learn from historical users' feedback that is often non-uniformly
distributed across items. As a consequence, these systems may end up suggesting popular …

This report aims to advance the discussion on those fundamental aspects to be considered
in order to have trustworthy Artificial Intelligence (AI) systems in the Automated/Autonomous …

In this paper we investigate risk prediction of criminal re-offense among juvenile defendants
using general-purpose machine learning (ML) algorithms. We show that in our dataset …

Gender inequality represents a considerable loss of human potential and perpetuates a
culture of violence, higher gender wage gaps, and a lack of representation of women in …

New emerging technologies powered by Artificial Intelligence (AI) have the potential to
disruptively transform our societies for the better. In particular, data-driven learning …

The literature addressing bias and fairness in AI models (fair-AI) is growing at a fast pace,
making it difficult for novel researchers and practitioners to have a bird's-eye view picture of …

Since the early phase of the artificial-intelligence (AI) era expectations towards AI are high,
with experts believing that AI paves the way for managing and handling various global …