Person:
Domínguez Catena, Iris

person.page.identifierURI

https://academica-e.unavarra.es/handle/2454/48907

Last Name

Domínguez Catena

First Name

Iris

person.page.departamento

Estadística, Informática y Matemáticas

person.page.instituteName

ISC. Institute of Smart Cities

ORCID

0000-0002-6099-8701

person.page.upna

811899

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Now showing 1 - 10 of 10

Open Access
Metrics for dataset demographic bias: a case study on facial expression recognition
(IEEE, 2024) Domínguez Catena, Iris; Paternain Dallo, Daniel; Galar Idoate, Mikel; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Universidad Pública de Navarra - Nafarroako Unibertsitate Publikoa
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 representation of demographic groups in the datasets. In this paper, we study the measurement of these biases by reviewing the existing metrics, including those that can be borrowed from other disciplines. We develop a taxonomy for the classification of these metrics, providing a practical guide for the selection of appropriate metrics. To illustrate the utility of our framework, and to further understand the practical characteristics of the metrics, we conduct a case study of 20 datasets used in Facial Emotion Recognition (FER), analyzing the biases present in them. Our experimental results show that many metrics are redundant and that a reduced subset of metrics may be sufficient to measure the amount of demographic bias. The paper provides valuable insights for researchers in AI and related fields to mitigate dataset bias and improve the fairness and accuracy of AI models.
Open Access
A study of OWA operators learned in convolutional neural networks
(MDPI, 2021) Domínguez Catena, Iris; Paternain Dallo, Daniel; Galar Idoate, Mikel; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Ordered Weighted Averaging (OWA) operators have been integrated in Convolutional Neural Networks (CNNs) for image classification through the OWA layer. This layer lets the CNN integrate global information about the image in the early stages, where most CNN architectures only allow for the exploitation of local information. As a side effect of this integration, the OWA layer becomes a practical method for the determination of OWA operator weights, which is usually a difficult task that complicates the integration of these operators in other fields. In this paper, we explore the weights learned for the OWA operators inside the OWA layer, characterizing them through their basic properties of orness and dispersion. We also compare them to some families of OWA operators, namely the Binomial OWA operator, the Stancu OWA operator and the expo-nential RIM OWA operator, finding examples that are currently impossible to generalize through these parameterizations.
Open Access
Additional feature layers from ordered aggregations for deep neural networks
(IEEE, 2020) Domínguez Catena, Iris; Paternain Dallo, Daniel; Galar Idoate, Mikel; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
In the last years we have seen huge advancements in the area of Machine Learning, specially with the use of Deep Neural Networks. One of the most relevant examples is in image classification, where convolutional neural networks have shown to be a vital tool, hard to replace with any other techniques. Although aggregation functions, such as OWA operators, have been previously used on top of neural networks, usually to aggregate the outputs of different networks or systems (ensembles), in this paper we propose and explore a new way of using OWA aggregations in deep learning. We implement OWA aggregations as a new layer inside a convolutional neural network. These layers are used to learn additional order-based information from the feature maps of a certain layer, and then the newly generated information is used as a complement input for the following layers. We carry out several tests introducing the new layer in a VGG13-based reference network and show that this layer introduces new knowledge into the network without substantially increasing training times.
Open Access
Diseño y captura de una base de datos para el reconocimiento de emociones minimizando sesgos
(CAEPIA, 2024) Jurío Munárriz, Aránzazu; Pascual Casas, Rubén; Domínguez Catena, Iris; Paternain Dallo, Daniel; Galar Idoate, Mikel; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertistate Publikoa; Gobierno de Navarra / Nafarroako Gobernua
El reconocimiento de emociones a partir de expresiones faciales (FER) es un campo de investigación importante para la interacción persona-máquina. Sin embargo, los conjuntos de datos utilizados para entrenar modelos FER a menudo contienen sesgos demográficos que pueden conducir a la discriminación en el modelo final. En este trabajo, presentamos el diseño y la captura realizados para la creación de una nueva base de datos para FER, donde tratamos de minimizar los sesgos desde el propio diseño. La base de datos se ha creado utilizando diferentes métodos de captura. Para comprobar la reducción de los sesgos alcanzada, analizamos diferentes métricas de sesgo representacional y estereotípico sobre la base de datos generada y la comparamos frente a otras bases de datos estándar en la literatura de FER.
Open Access
Demographic bias in machine learning: measuring transference from dataset bias to model predictions
(2024) Domínguez Catena, Iris; Galar Idoate, Mikel; Paternain Dallo, Daniel; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
As artificial intelligence (AI) systems increasingly influence critical decisions in society, ensuring fairness and avoiding bias have become pressing challenges. This dissertation investigates demographic bias in machine learning, with a particular focus on measuring how bias transfers from datasets to model predictions. Using Facial Expression Recognition (FER) as a primary case study, we develop novel metrics and methodologies to quantify and analyze bias at both the dataset and model levels. The thesis makes several key contributions to the field of algorithmic fairness. We propose a comprehensive taxonomy of types of dataset bias and metrics available for each type. Through extensive evaluation on FER datasets, we demonstrate the effectiveness and limitations of these metrics in capturing different aspects of demographic bias. Additionally, we introduce DSAP (Demographic Similarity from Auxiliary Profiles), a novel method for comparing datasets based on their demographic properties. DSAP enables interpretable bias measurement and analysis of demographic shifts between datasets, providing valuable insights for dataset curation and model development. Our research includes in-depth experiments examining the propagation of representational and stereotypical biases from datasets to FER models. Our findings reveal that while representational bias tends to be mitigated during model training, stereotypical bias is more likely to persist in model predictions. Furthermore, we present a framework for measuring bias transference from datasets to models across various bias induction scenarios. This analysis uncovers complex relationships between dataset bias and resulting model bias, highlighting the need for nuanced approaches to bias mitigation. Throughout the dissertation, we emphasize the importance of considering both representational and stereotypical biases in AI systems. Our work demonstrates that these biases can manifest and propagate differently, necessitating tailored strategies for detection and mitigation. By providing robust methodologies for quantifying and analyzing demographic bias, this research contributes to the broader goal of developing fairer and more equitable AI systems. The insights and tools presented here have implications beyond FER, offering valuable approaches for addressing bias in various machine learning applications. This dissertation paves the way for future work in algorithmic fairness, emphasizing the need for continued research into bias measurement, mitigation strategies, and the development of more inclusive AI technologies.
Open Access
Learning channel-wise ordered aggregations in deep neural networks
(Springer, 2021) Domínguez Catena, Iris; Paternain Dallo, Daniel; Galar Idoate, Mikel; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
One of the most common techniques for approaching image classification problems are Deep Neural Networks. These systems are capable of classifying images with different levels of detail at different levels of detail, with an accuracy that sometimes can surpass even manual classification by humans. Most common architectures for Deep Neural Networks are based on convolutional layers, which perform at the same time a convolution on each input channel and a linear aggregation on the convoluted channels. In this work, we develop a new method for augmenting the information of a layer inside a Deep Neural Network using channel-wise ordered aggregations. We develop a new layer that can be placed at different points inside a Deep Neural Network. This layer takes the feature maps of the previous layer and adds new feature maps by applying several channel-wise ordered aggregations based on learned weighting vectors. We perform several experiments introducing this layer in a VGG neural network and study the impact of the new layer, obtaining better accuracy scores over a sample dataset based on ImageNet. We also study the convergence and evolution of the weighting vectors of the new layers over the learning process, which gives a better understanding of the way the system is exploiting the additional information to gain new knowledge.
Open Access
Unsupervised fuzzy measure learning for classifier ensembles from coalitions performance
(IEEE, 2020) Uriz Martín, Mikel Xabier; Paternain Dallo, Daniel; Domínguez Catena, Iris; Bustince Sola, Humberto; Galar Idoate, Mikel; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, PJUPNA13
In Machine Learning an ensemble refers to the combination of several classifiers with the objective of improving the performance of every one of its counterparts. To design an ensemble two main aspects must be considered: how to create a diverse set of classifiers and how to combine their outputs. This work focuses on the latter task. More specifically, we focus on the usage of aggregation functions based on fuzzy measures, such as the Sugeno and Choquet integrals, since they allow to model the coalitions and interactions among the members of the ensemble. In this scenario the challenge is how to construct a fuzzy measure that models the relations among the members of the ensemble. We focus on unsupervised methods for fuzzy measure construction, review existing alternatives and categorize them depending on their features. Furthermore, we intend to address the weaknesses of previous alternatives by proposing a new construction method that obtains the fuzzy measure directly evaluating the performance of each possible subset of classifiers, which can be efficiently computed. To test the usefulness of the proposed fuzzy measure, we focus on the application of ensembles for imbalanced datasets. We consider a set of 66 imbalanced datasets and develop a complete experimental study comparing the reviewed methods and our proposal.
Open Access
Simultaneous generation of surface plasmon and lossy mode resonances in the same planar platform
(MDPI, 2022) Fuentes Lorenzo, Omar; Del Villar, Ignacio; Domínguez Catena, Iris; Corres Sanz, Jesús María; Matías Maestro, Ignacio; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Institute of Smart Cities - ISC; Ingeniería Eléctrica, Electrónica y de Comunicación; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
A planar waveguide consisting of a coverslip for a microscope glass slide was deposited in one of its two faces with two materials: silver and indium tin oxide (ITO). The incidence of light by the edge of the coverslip permitted the generation of both surface plasmon and lossy mode resonances (SPRs and LMRs) in the same transmission spectrum with a single optical source and detector. This proves the ability of this optical platform to be used as a benchmark for comparing different optical phenomena generated by both metal and dielectric materials, which can be used to progress in the assessment of different sensing technologies. Here the SPR and the LMR were compared in terms of sensitivity to refractive index and figure of merit (FoM), at the same time it was demonstrated that both resonances can operate independently when silver and ITO coated regions are surrounded by different refractive index liquids. The results were supported with numerical results that confirm the experimental ones.
Open Access
Gender stereotyping impact in facial expression recognition
(Springer, 2023) Domínguez Catena, Iris; Paternain Dallo, Daniel; Galar Idoate, Mikel; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
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, such as apparent age, gender, and race of the subject, these systems are prone to demographic bias issues. In recent years, machine learning-based models have become the most popular approach to FER. These models require training on large datasets of facial expression images, and their generalization capabilities are strongly related to the characteristics of the dataset. In publicly available FER datasets, apparent gender representation is usually mostly balanced, but their representation in the individual label is not, embedding social stereotypes into the datasets and generating a potential for harm. Although this type of bias has been overlooked so far, it is important to understand the impact it may have in the context of FER. To do so, we use a popular FER dataset, FER+, to generate derivative datasets with different amounts of stereotypical bias by altering the gender proportions of certain labels. We then proceed to measure the discrepancy between the performance of the models trained on these datasets for the apparent gender groups. We observe a discrepancy in the recognition of certain emotions between genders of up to 29 % under the worst bias conditions. Our results also suggest a safety range for stereotypical bias in a dataset that does not appear to produce stereotypical bias in the resulting model. Our findings support the need for a thorough bias analysis of public datasets in problems like FER, where a global balance of demographic representation can still hide other types of bias that harm certain demographic groups.
Open Access
Capas basadas en operadores OWA para Redes Neuronales Convolucionales
(2020) Domínguez Catena, Iris; Galar Idoate, Mikel; Paternain Dallo, Daniel; Escuela Técnica Superior de Ingeniería Industrial, Informática y de Telecomunicación; Industria, Informatika eta Telekomunikazio Ingeniaritzako Goi Mailako Eskola Teknikoa
En este trabajo exploramos una nueva forma de ampliar la capacidad de las Redes Neuronales Convolucionales. En concreto, planteamos una nueva t´ecnica para generar informaci´on adicional a partir de la salida de un bloque convolucional de una Red Neuronal Convolucional, empleando para ello operadores OWA a nivel de canal, y usando esta nueva informaci´on para ampliar la entrada de las siguientes capas de la red. Realizamos diversas pruebas con esta nueva t´ecnica, comprobando como afectan diferentes par´ametros a los resultados, incluyendo el punto de inserci´on de la nueva informaci´on, la cantidad de operadores OWA aplicados, o el tipo de m´etrica empleada para ordenar los canales de informaci´on original.