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This work has been granted by the "EICACS (European Initiative for Collaborative Air Combat Standardisation) "project of the Horizon Europe programme of the European Commission, under grant agreement No. 101103669.

Analysis of institutional authors

Gonzalez-Sendino, RubenAuthorSerrano, EmilioCorresponding AuthorBajo, JavierAuthor

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April 28, 2024
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Mitigating bias in artificial intelligence: Fair data generation via causal models for transparent and explainable decision-making

Publicated to:Future Generation Computer Systems-The International Journal Of Escience. 155 384-401 - 2024-06-01 155(), DOI: 10.1016/j.future.2024.02.023

Authors: González-Sendino, R; Serrano, E; Bajo, J

Affiliations

Univ Politecn Madrid, Ontol Engn Grp, ETSI Informat, Madrid 28660, Spain - Author

Abstract

In the evolving field of Artificial Intelligence, concerns have arisen about the opacity of certain models and their potential biases. This study aims to improve fairness and explainability in AI decision making. Existing bias mitigation strategies are classified as pre-training, training, and post-training approaches. This paper proposes a novel technique to create a mitigated bias dataset. This is achieved using a mitigated causal model that adjusts cause-and-effect relationships and probabilities within a Bayesian network. Contributions of this work include (1) the introduction of a novel mitigation training algorithm for causal model; (2) a pioneering pretraining methodology for producing a fair dataset for Artificial Intelligence model training; (3) the diligent maintenance of sensitive features in the dataset, ensuring that these vital attributes are not overlooked during analysis and model training; (4) the enhancement of explainability and transparency around biases; and finally (5) the development of an interactive demonstration that vividly displays experimental results and provides the code for facilitating replication of the work.

Keywords

Artificial intelligenceBayeBayesBayesian networksBias mitigationCausal modelCausal modelingData generationDecision makingDecisions makingsDistributionFairnessMitigation strategyModel trainingPre-trainingResponsible artificial intelligence

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Future Generation Computer Systems-The International Journal Of Escience due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 15/147, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Theory & Methods. Notably, the journal is positioned above the 90th percentile.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-09-10:

  • WoS: 8
  • Scopus: 33

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 2025-09-10:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 113.
  • 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: 113 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 16.65.
  • The number of mentions on the social network X (formerly Twitter): 30 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/84162/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 136
  • Downloads: 54

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 (GONZÁLEZ SENDINO, RUBÉN) and Last Author (BAJO PEREZ, JAVIER).

the author responsible for correspondence tasks has been SERRANO FERNANDEZ, EMILIO.