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Framework for Detecting Algorithmic Bias in the Development of Artificial Intelligence Applications A.I. Case Study: Artificial Intelligence Laboratory-UPTC Facultad Seccional Sogamoso-Colombia

In: Management, Tourism, and Smart Technologies, Vol 2

Author

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  • Marco Javier Suarez Baron

    (UPTC - Universidad Pedagógica y Tecnológica de Colombia)

Abstract

This article sets out a framework for identifying biases in the applications and uses of AI and the development of AI algorithms, analysing their causes, manifestations and consequences for the social context. It studies how biases can be unexpectedly introduced into algorithms during the stages of data collection, preprocessing and model training, which can generate discriminatory results towards certain social groups. Methodologies and frameworks designed to reduce these biases, such as equity-sensitive algorithms, bias identification techniques and strategies to promote diversity, are explored. The proposal addresses ethical considerations and regulatory efforts, highlighting the urgent need for transparency and accountability in AI development.

Suggested Citation

  • Marco Javier Suarez Baron, 2026. "Framework for Detecting Algorithmic Bias in the Development of Artificial Intelligence Applications A.I. Case Study: Artificial Intelligence Laboratory-UPTC Facultad Seccional Sogamoso-Colombia," Springer Proceedings in Business and Economics, in: Pedro Miguel Gaspar & José Machado & João Paulo Ramos Teixeira & José Avelino Moreira Victor & Carlo (ed.), Management, Tourism, and Smart Technologies, Vol 2, chapter 9, pages 111-121, Springer.
    • Handle: RePEc:spr:prbchp:978-3-032-24600-4_9
    DOI: 10.1007/978-3-032-24600-4_9
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