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TOWARDS EXPLAINABLE CLASSIFIERS USING THE COUNTERFACTUAL APPROACH - GLOBAL EXPLANATIONS FOR DISCOVERING BIAS IN DATA
Abstract
The paper proposes summarized attribution-based post-hoc explanations for the detection and identification of bias in data. A global explanation is proposed, and a step-by-step framework on how to detect and test bias is introduced. Since removing unwanted bias is often a complicated and tremendous task, it is automatically inserted, instead. Then, the bias is evaluated with the proposed counterfactual approach. The obtained results are validated on a sample skin lesion dataset. Using the proposed method, a number of possible bias-causing artifacts are successfully identified and confirmed in dermoscopy images. In particular, it is confirmed that black frames have a strong influence on Convolutional Neural Network’s prediction: 22% of them changed the prediction from benign to malignant.
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
Journal of Artificial Intelligence and Soft Computing Research
no. 11,
pages 51 - 67,
ISSN: 2083-2567 - Language:
- English
- Publication year:
- 2021
- Bibliographic description:
- Mikołajczyk A., Grochowski M., Kwasigroch A.: TOWARDS EXPLAINABLE CLASSIFIERS USING THE COUNTERFACTUAL APPROACH - GLOBAL EXPLANATIONS FOR DISCOVERING BIAS IN DATA// Journal of Artificial Intelligence and Soft Computing Research -Vol. 11,iss. 1 (2021), s.51-67
- DOI:
- Digital Object Identifier (open in new tab) 10.2478/jaiscr-2021-0004
- Verified by:
- Gdańsk University of Technology
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