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

Marco Javier Suarez Baron ()
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Marco Javier Suarez Baron: UPTC - Universidad Pedagógica y Tecnológica de Colombia

Chapter 9 in Management, Tourism, and Smart Technologies, Vol 2, 2026, pp 111-121 from Springer

Abstract: 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.

Keywords: Artificial Intelligence A.I.; Algorithmic bias; Society; Decision making (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-032-24600-4_9

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DOI: 10.1007/978-3-032-24600-4_9

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