64 citations · 105 across the 6 of their papers we have counts for
7 papers
Biased Heritage: How Datasets Shape Models in Facial Expression Recognition
Iris Dominguez-Catena, Daniel Paternain, Mikel Galar +3
In recent years, the rapid development of artificial intelligence (AI) systems has raised concerns about our ability to ensure their fairness, that is, how to avoid discrimination…
Large Language Models Reflect the Ideology of their Creators
Maarten Buyl, Alexander Rogiers, Sander Noels +8
Large language models (LLMs) are trained on vast amounts of data to generate natural language, enabling them to perform tasks like text summarization and question answering. These…
Less can be more: representational vs. stereotypical gender bias in facial expression recognition
Iris Dominguez-Catena, Daniel Paternain, Aranzazu Jurio +1
Machine learning models can inherit biases from their training data, leading to discriminatory or inaccurate predictions. This is particularly concerning with the increasing use of…
DSAP: Analyzing Bias Through Demographic Comparison of Datasets
Iris Dominguez-Catena, Daniel Paternain, Mikel Galar
In the last few years, Artificial Intelligence systems have become increasingly widespread. Unfortunately, these systems can share many biases with human decision-making, including…
Metrics for Dataset Demographic Bias: A Case Study on Facial Expression Recognition
Iris Dominguez-Catena, Daniel Paternain, Mikel Galar
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 most prominent…
Gender Stereotyping Impact in Facial Expression Recognition
Iris Dominguez-Catena, Daniel Paternain, Mikel Galar
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. Unfortun…
Assessing Demographic Bias Transfer from Dataset to Model: A Case Study in Facial Expression Recognition
Iris Dominguez-Catena, Daniel Paternain, Mikel Galar
The increasing amount of applications of Artificial Intelligence (AI) has led researchers to study the social impact of these technologies and evaluate their fairness. Unfortunatel…