Explainable Artificial Intelligence in an application in Recommendation Systems
DOI:
https://doi.org/10.46480/esj.8.2.180Keywords:
recommendation system, algorithm, prototype, prediction, machine learning, tendersAbstract
This article focuses on the development of interpretive techniques for an Artificial Intelligence (AI)-based recommendation system applied to public procurement processes. The project seeks not only to implement technical solutions, but also to address structural and organizational challenges in procurement, improving efficiency and fairness. It highlights the exponential growth of technological dependence in various sectors, driven by advances in AI and Machine Learning, and the adoption of Explainable Artificial Intelligence (XAI). Unlike traditional AI, XAI balances accuracy with human interpretability, crucial for its application in recommendation systems. This holistic approach aims to improve transparency, trust, and efficiency in supplier selection, addressing opacity and
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A. M. Gonzalo, «Universidad Politécnica de Madrid,» Junio 2022. [En línea]. Available: https://oa.upm.es/71271/.
C. Janiesch, « Machine learning and deep learning,» abril 2021. [En línea]. Available: https://link.springer.com/article/10.1007/s12525-021-00475-2. DOI: https://doi.org/10.1007/s12525-021-00475-2
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Copyright (c) 2024 Miguel Molina Villacís, María Molina Miranda, Ximena Acaro Chacón, Angel Jiménez Villao , Darla Luna Chiriboga
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