Artificial intelligence (AI) and deep learning (DL) have shown tremendous potential in
driving sustainability across various sectors. This paper reviews recent advancements in AI …

Purpose Gender bias in artificial intelligence (AI) should be solved as a priority before AI
algorithms become ubiquitous, perpetuating and accentuating the bias. While the problem …

Deep neural networks are hegemonic approaches to many machine learning areas,
including natural language processing (NLP). Thanks to the availability of large corpora …

Large language models (LLMs) have the potential to transform our lives and work through
the content they generate, known as AI-Generated Content (AIGC). To harness this …

Data-driven algorithms are studied and deployed in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing community of …

Critical scholarship has elevated the problem of gender bias in data sets used to train virtual
assistants (VAs). Most work has focused on explicit biases in language, especially against …

Ethics of Artificial Intelligence (AI) is a growing research field that has emerged in response
to the challenges related to AI. Transparency poses a key challenge for implementing AI …

The unprecedented increase in computing power and data availability has signifi-cantly
altered the way and the scope that organizations make decisions relying on technologies …

Audio-visual learning seeks to enhance the computer's multi-modal perception leveraging
the correlation between the auditory and visual modalities. Despite their many useful …

A growing community of researchers has been investigating the equity of algorithms,
advancing the understanding of risks and opportunities of automated decision-making for …