64 citations · 93 across the 9 of their papers we have counts for
10 papers
Unmasking LAION-5B: Age, Gender, Race, and Emotion Biases in Large-Scale Image Datasets
Iris Dominguez-Catena, Daniel Paternain, Mikel Galar
Large-scale image-text datasets, such as LAION-5B, are foundational to modern AI systems, yet their vast scale and uncurated nature raise significant concerns about demographic and…
Evaluating Histogram Matching for Robust Deep learning-Based Grapevine Disease Detection
Ruben Pascual, Inés Hernández, Salvador Gutiérrez +4
Variability in illumination is a primary factor limiting deep learning robustness for field-based plant disease detection. This study evaluates Histogram Matching (HM), a technique…
CA-TCN: A Causal-Anticausal Temporal Convolutional Network for Direct Auditory Attention Decoding
Iñigo García-Ugarte, Rubén Eguinoa, Ricardo San Martín +2
A promising approach for steering auditory attention in complex listening environments relies on Auditory Attention Decoding (AAD), which aim to identify the attended speech stream…
Few-shot multi-token DreamBooth with LoRa for style-consistent character generation
Ruben Pascual, Mikel Sesma-Sara, Aranzazu Jurio +2
The audiovisual industry is undergoing a profound transformation as it is integrating AI developments not only to automate routine tasks but also to inspire new forms of art. This…
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…
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…