DSAP: analyzing bias through demographic comparison of datasets

In the last few years, Artificial Intelligence (AI) 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 data used for training, where large uncurated da...

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Autores: Domínguez Catena, Iris, Paternain Dallo, Daniel, Galar Idoate, Mikel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universidad Pública de Navarra
Repositorio:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/53572
Acceso en línea:https://hdl.handle.net/2454/53572
Access Level:acceso abierto
Palabra clave:Artificial Intelligence
Deep Learning
Facial expression recognition
Demographic bias
Dataset analysis
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spelling DSAP: analyzing bias through demographic comparison of datasetsDomínguez Catena, IrisPaternain Dallo, DanielGalar Idoate, MikelArtificial IntelligenceDeep LearningFacial expression recognitionDemographic biasDataset analysisIn the last few years, Artificial Intelligence (AI) 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 data used for training, where large uncurated datasets have become the norm. Despite our awareness of these biases, we still lack general tools to detect, quantify, and compare them across different datasets. In this work, we propose DSAP (Demographic Similarity from Auxiliary Profiles), a two-step methodology for comparing the demographic composition of datasets. First, DSAP uses existing demographic estimation models to extract a dataset's demographic profile. Second, it applies a similarity metric to compare the demographic profiles of different datasets. While these individual components are well-known, their joint use for demographic dataset comparison is novel and has not been previously addressed in the literature. This approach allows three key applications: the identification of demographic blind spots and bias issues across datasets, the measurement of demographic bias, and the assessment of demographic shifts over time. DSAP can be used on datasets with or without explicit demographic information, provided that demographic information can be derived from the samples using auxiliary models, such as those for image or voice datasets. To show the usefulness of the proposed methodology, we consider the Facial Expression Recognition task, where demographic bias has previously been found. The three applications are studied over a set of twenty datasets with varying properties. The code is available at https://github.com/irisdominguez/DSAP.This work was funded by a predoctoral fellowship from the Research Service of the Universidad Publica de Navarra, the Spanish MICIN (PID2020-118014RB-I00 and PID2022-136627NB-I00/AEI/10.13039/501100011033 FEDER, UE), and the Government of Navarre (0011-1411-2020-000079 - Emotional Films). Open access funding provided by Universidad Pública de Navarra.ElsevierEstadística, Informática y MatemáticasEstatistika, Informatika eta MatematikaUniversidad Publica de Navarra / Nafarroako Unibertsitate Publikoa Gobierno de Navarra / Nafarroako Gobernua2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2454/53572reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118014RB-I00info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-136627NB-I00info:eu-repo/grantAgreement/Gobierno de Navarra//0011-1411-2020-000079© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/535722026-06-17T12:41:47Z
dc.title.none.fl_str_mv DSAP: analyzing bias through demographic comparison of datasets
title DSAP: analyzing bias through demographic comparison of datasets
spellingShingle DSAP: analyzing bias through demographic comparison of datasets
Domínguez Catena, Iris
Artificial Intelligence
Deep Learning
Facial expression recognition
Demographic bias
Dataset analysis
title_short DSAP: analyzing bias through demographic comparison of datasets
title_full DSAP: analyzing bias through demographic comparison of datasets
title_fullStr DSAP: analyzing bias through demographic comparison of datasets
title_full_unstemmed DSAP: analyzing bias through demographic comparison of datasets
title_sort DSAP: analyzing bias through demographic comparison of datasets
dc.creator.none.fl_str_mv Domínguez Catena, Iris
Paternain Dallo, Daniel
Galar Idoate, Mikel
author Domínguez Catena, Iris
author_facet Domínguez Catena, Iris
Paternain Dallo, Daniel
Galar Idoate, Mikel
author_role author
author2 Paternain Dallo, Daniel
Galar Idoate, Mikel
author2_role author
author
dc.contributor.none.fl_str_mv Estadística, Informática y Matemáticas
Estatistika, Informatika eta Matematika
Universidad Publica de Navarra / Nafarroako Unibertsitate Publikoa
Gobierno de Navarra / Nafarroako Gobernua
dc.subject.none.fl_str_mv Artificial Intelligence
Deep Learning
Facial expression recognition
Demographic bias
Dataset analysis
topic Artificial Intelligence
Deep Learning
Facial expression recognition
Demographic bias
Dataset analysis
description In the last few years, Artificial Intelligence (AI) 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 data used for training, where large uncurated datasets have become the norm. Despite our awareness of these biases, we still lack general tools to detect, quantify, and compare them across different datasets. In this work, we propose DSAP (Demographic Similarity from Auxiliary Profiles), a two-step methodology for comparing the demographic composition of datasets. First, DSAP uses existing demographic estimation models to extract a dataset's demographic profile. Second, it applies a similarity metric to compare the demographic profiles of different datasets. While these individual components are well-known, their joint use for demographic dataset comparison is novel and has not been previously addressed in the literature. This approach allows three key applications: the identification of demographic blind spots and bias issues across datasets, the measurement of demographic bias, and the assessment of demographic shifts over time. DSAP can be used on datasets with or without explicit demographic information, provided that demographic information can be derived from the samples using auxiliary models, such as those for image or voice datasets. To show the usefulness of the proposed methodology, we consider the Facial Expression Recognition task, where demographic bias has previously been found. The three applications are studied over a set of twenty datasets with varying properties. The code is available at https://github.com/irisdominguez/DSAP.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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dc.identifier.none.fl_str_mv https://hdl.handle.net/2454/53572
url https://hdl.handle.net/2454/53572
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118014RB-I00
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-136627NB-I00
info:eu-repo/grantAgreement/Gobierno de Navarra//0011-1411-2020-000079
dc.rights.none.fl_str_mv https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
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