New Explainability Method based on the Classification of Useful Regions in an Image

Authors

  • Tonantzin Marcayda Guerrero Velázquez Instituto Politécnico Nacional, Centro de Investigación en Computación
  • Juan Humberto Sossa Azuela Instituto Politécnico Nacional, Centro de Investigación en Computación

DOI:

https://doi.org/10.13053/cys-25-4-4049

Keywords:

Explainability, classifier, XAI, machine learning

Abstract

Machine learning is a necessary and widely used tool nowadays in industry. Talking about the evaluation of its reliability, already known metrics are broadly used, but they are focused on how precise, accurate or sensitive the model is. Nevertheless, these metrics do not offer an overview of the consistency or stability of the predictions, that is, how much reliable the model is, which could be deduced if the reasons behind the predictions are understood. In the present work, we propose a novel method that can be applied to image classifiers and allows the understanding, in a non-subjective visual manner, of the background of a prediction.

Author Biographies

Tonantzin Marcayda Guerrero Velázquez, Instituto Politécnico Nacional, Centro de Investigación en Computación

Laboratorio de Robótica y Mecatrónica

Juan Humberto Sossa Azuela, Instituto Politécnico Nacional, Centro de Investigación en Computación

Laboratorio de Robótica y Mecatrónica

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Published

2021-11-29

Issue

Vol. 25 No. 4 (2021): 25(4) 2021: Thematic Issue on Artificial Intelligence for Industry 4.0 (Guest Editors: O. Vergara et al.)

Section

Articles of the Thematic Issue

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