Towards explainable classifiers using the counterfactual approach : global explanations for discovering bias in data

• Autorzy: Mikołajczyk Agnieszka , Grochowski Michał , Kwasigroch Arkadiusz

Identyfikatory

DOI

10.2478/jaiscr-2021-0004

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

explainable classifiers; counterfactual approach; bias detection

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2021
• Tom: Vol. 11, No. 1
• Strony: 51--67
• Opis fizyczny: Bibliogr. 42 poz., rys.

Twórcy

autor

Mikołajczyk Agnieszka

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

autor

Grochowski Michał

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

autor

Kwasigroch Arkadiusz

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

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).