Domínguez Catena, Iris

Profile Picture

Email Address

person.page.identifierURI

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

Birth Date

Job Title

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.observainves

751293

person.page.upna

811899

Name

Filters

20241
Reset filters

Settings

Author: Galar Idoate, Mikel × Subject: Artificial Intelligence ×

Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    PublicationOpen Access
    DSAP: analyzing bias through demographic comparison of datasets
    (Elsevier, 2024-10-29) Domínguez Catena, Iris; Paternain Dallo, Daniel; Galar Idoate, Mikel; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Universidad Publica de Navarra / Nafarroako Unibertsitate Publikoa ; Gobierno de Navarra / Nafarroako Gobernua
    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.