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Evaluative Customized Naïve Associative Classifier: Promoting Equity in AI for the Selection and Promotion of Human Resources

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Intelligent Data Engineering and Automated Learning – IDEAL 2024 (IDEAL 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15347))

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Abstract

Fairness and bias in AI for human resource selection and promotion are complex issues that require careful and continued attention. While AI has the potential to improve equity by eliminating subjective human bias, it also presents significant risks if inherent biases in data and algorithms are not adequately addressed. By prioritizing equity, transparency and diversity, we can harness the power of AI to promote fairer and more inclusive work environments. This article introduces the Evaluative Customized Naïve Associative Classifier as an alternative to promote equity and transparency in the automatic selection and promotion processes of human resources. The results obtained support the viability of the proposal within the Evaluative Artificial Intelligence approach.

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Acknowledgments

The authors would like to thank the Instituto Politécnico Nacional (Secretaría Académica, Secretaría de Investigación y Posgrado, Centro de Investigación en Computación, and Centro de Innovación y Desarrollo Tecnológico en Cómputo), the Consejo Nacional de Ciencia y Tecnología, and Sistema Nacional de Investigadores for their support to develop this work. The authors would also want to thank Mondragon Unibertsitatea, Faculty of Engineering.

Author information

Authors and Affiliations

  1. Instituto Politécnico Nacional, CIC-IPN, 07738, Av. Juan de Dios Bátiz s/n, GAM, Mexico City, Mexico

    Claudia C. Tusell-Rey

  2. Faculty of Engineering, Mondragon Unibertsitatea, Loramendi, 4. Post office box 23, 20500, Arrasate – Mondragón, Spain

    Joel Pino-Gómez

  3. Instituto Politécnico Nacional, CIDETECC-IPN, 07700, Av. Juan de Dios Bátiz s/n, GAM, Mexico City, Mexico

    Yenny Villuendas-Rey

Authors
  1. Claudia C. Tusell-Rey
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  2. Joel Pino-Gómez
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  3. Yenny Villuendas-Rey
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Corresponding author

Correspondence to Yenny Villuendas-Rey .

Editor information

Editors and Affiliations

  1. Technical University of Valencia, Valencia, Valencia, Spain

    Vicente Julian

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. The University of Manchester, Manchester, UK

    Hujun Yin

  4. Universitat Politècnica de València, Valencia, Valencia, Spain

    Juan M. Alberola

  5. University of Evora, Evora, Portugal

    Vitor Beires Nogueira

  6. Universidade do Minho, Braga, Portugal

    Paulo Novais

  7. University of Huelva, Huelva, Spain

    Antonio Tallón-Ballesteros

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The authors have no competing interests to declare that are relevant to the content of this article.

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Tusell-Rey, C.C., Pino-Gómez, J., Villuendas-Rey, Y. (2025). Evaluative Customized Naïve Associative Classifier: Promoting Equity in AI for the Selection and Promotion of Human Resources. In: Julian, V., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2024. IDEAL 2024. Lecture Notes in Computer Science, vol 15347. Springer, Cham. https://doi.org/10.1007/978-3-031-77738-7_23

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  • DOI: https://doi.org/10.1007/978-3-031-77738-7_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-77737-0

  • Online ISBN: 978-3-031-77738-7

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