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pro vyhledávání: '"Paternain, Daniel"'
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 large, unsupervised datasets for training foundational models. Tradit
Externí odkaz:
http://arxiv.org/abs/2406.17405
In the last few years, Artificial Intelligence systems have become increasingly widespread. Unfortunately, these systems can share many biases with human decision-making, including demographic biases. Often, these biases can be traced back to the dat
Externí odkaz:
http://arxiv.org/abs/2312.14626
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 types of demographic bias are statistical imbalances in the represent
Externí odkaz:
http://arxiv.org/abs/2303.15889
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. Unfortunately, as the images naturally integrate some demographic information,
Externí odkaz:
http://arxiv.org/abs/2210.05332
Publikováno v:
Proceedings of the Workshop on Artificial Intelligence Safety 2022 (AISafety 2022)
The increasing amount of applications of Artificial Intelligence (AI) has led researchers to study the social impact of these technologies and evaluate their fairness. Unfortunately, current fairness metrics are hard to apply in multi-class multi-dem
Externí odkaz:
http://arxiv.org/abs/2205.10049
Publikováno v:
In Information Fusion March 2025 115
Publikováno v:
In Information Sciences April 2023 622:490-511
Akademický článek
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Publikováno v:
In Fuzzy Sets and Systems 5 August 2022 441:169-195
Autor:
Palmeira, Eduardo S., Bedregal, Benjamin, Bustince, Humberto, Paternain, Daniel, Miguel, Laura De
Publikováno v:
In Information Sciences May 2018 441:95-112