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Analysis of institutional authors

Peña ACorresponding AuthorSerna IAuthorMorales AAuthorFierrez JAuthor

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September 14, 2020
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Proceedings Paper
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Bias in multimodal AI: Testbed for fair automatic recruitment

Publicated to:IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. 2020-June 129-137 - 2020-06-01 2020-June(), DOI: 10.1109/CVPRW50498.2020.00022

Authors: Pena, Alejandro; Serna, Ignacio; Morales, Aythami; Fierrez, Julian

Affiliations

Univ Autonoma Madrid, Sch Engn, Madrid, Spain - Author
Universidad Autónoma de Madrid - Author

Abstract

© 2020 IEEE. The presence of decision-making algorithms in society is rapidly increasing nowadays, while concerns about their transparency and the possibility of these algorithms becoming new sources of discrimination are arising. In fact, many relevant automated systems have been shown to make decisions based on sensitive information or discriminate certain social groups (e.g. certain biometric systems for person recognition). With the aim of studying how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data, we propose a fictitious automated recruitment testbed: FairCVtest. We train automatic recruitment algorithms using a set of multimodal synthetic profiles consciously scored with gender and racial biases. Fair-CVtest shows the capacity of the Artificial Intelligence (AI) behind such recruitment tool to extract sensitive information from unstructured data, and exploit it in combination to data biases in undesirable (unfair) ways. Finally, we present a list of recent works developing techniques capable of removing sensitive information from the decision-making process of deep learning architectures. We have used one of these algorithms (SensitiveNets) to experiment discrimination-aware learning for the elimination of sensitive information in our multimodal AI framework. Our methodology and results show how to generate fairer AI-based tools in general, and in particular fairer automated recruitment systems.

Keywords

Computer vision and pattern recognitionElectrical and electronic engineering

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2020, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Vision and Pattern Recognition. Notably, the journal is positioned above the 90th percentile.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations from Scopus Elsevier, it yields a value for the Field-Weighted Citation Impact from the Scopus agency: 1.96, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Field Citation Ratio (FCR) from Dimensions: 13.71 (source consulted: Dimensions Jul 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-04, the following number of citations:

  • WoS: 24
  • Scopus: 36

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-04:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 140 (PlumX).

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (PEÑA ALMANSA, ALEJANDRO) and Last Author (FIERREZ AGUILAR, JULIAN).

the author responsible for correspondence tasks has been PEÑA ALMANSA, ALEJANDRO.