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Towards Explainable Classifiers Using the Counterfactual Approach - Global Explanations for Discovering Bias in Data Cover

Towards Explainable Classifiers Using the Counterfactual Approach - Global Explanations for Discovering Bias in Data

Journal of Artificial Intelligence and Soft Computing Research
Volume 11 (2021): Issue 1 (January 2021)
By: Agnieszka Mikołajczyk,  Michał Grochowski and  Arkadiusz Kwasigroch  
Open Access
|Dec 2020

Authors

Agnieszka Mikołajczyk

agnieszka.mikolajczyk@pg.edu.pl

Department of Electrical Engineering, Control Systems and Informatics, Gdansk University of Technology, Poland

Michał Grochowski

Department of Electrical Engineering, Control Systems and Informatics, Gdansk University of Technology, Poland

Arkadiusz Kwasigroch

Department of Electrical Engineering, Control Systems and Informatics, Gdansk University of Technology, Poland
DOI: https://doi.org/10.2478/jaiscr-2021-0004
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Language: English
Page range: 51 - 67
Submitted on: May 15, 2020
|
Accepted on: Sep 30, 2020
|
Published on: Dec 3, 2020
Published by: SAN University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
explainable classifiers,
counterfactual approach,
bias detection
Related subjects:
Computer sciences,
Databases and data mining,
Artificial intelligence

© 2020 Agnieszka Mikołajczyk, Michał Grochowski, Arkadiusz Kwasigroch, published by SAN University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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