Ethics in AI has become a debated topic of public and expert discourse in recent years. But
what do people who build AI—AI practitioners—have to say about their understanding of AI …

Demographic biases in source datasets have been shown as one of the causes of
unfairness and discrimination in the predictions of Machine Learning models. One of the …

Abstract Facial Expression Recognition (FER) uses images of faces to identify the emotional
state of users, allowing for a closer interaction between humans and autonomous systems …

Facial Expression Recognition (FER) can achieve an understanding of the emotional
changes of a specific target group. The relatively small dataset related to facial expression …

Machine learning models can inherit biases from their training data, leading to
discriminatory or inaccurate predictions. This is particularly concerning with the increasing …

In the last few years, Artificial Intelligence systems have become increasingly widespread.
Unfortunately, these systems can share many biases with human decision-making, including …

This study delves into gender classification systems, shedding light on the interaction
between social stereotypes and algorithmic determinations. Drawing on the" averageness …

Facial emotion recognition (FER) is among computer vision's most complex fields and has
practical uses in human-computer interaction (HCI) and psychology. Currently, FER models …