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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\u2019s prediction: 22% of them changed the prediction from benign to malignant.<\/jats:p>","DOI":"10.2478\/jaiscr-2021-0004","type":"journal-article","created":{"date-parts":[[2020,12,22]],"date-time":"2020-12-22T07:29:59Z","timestamp":1608622199000},"page":"51-67","source":"Crossref","is-referenced-by-count":23,"title":["Towards Explainable Classifiers Using the Counterfactual Approach - Global Explanations for Discovering Bias in Data"],"prefix":"10.2478","volume":"11","author":[{"given":"Agnieszka","family":"Miko\u0142ajczyk","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Control Systems and Informatics , Gdansk University of Technology , Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Micha\u0142","family":"Grochowski","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Control Systems and Informatics , Gdansk University of Technology , Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arkadiusz","family":"Kwasigroch","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Control Systems and Informatics , Gdansk University of Technology , Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2020,12,3]]},"reference":[{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_001_w2aab3b7b8b1b6b1ab1ab1Aa","doi-asserted-by":"crossref","unstructured":"[1] P. Stock and M. Cisse, ConvNets and imagenet beyond accuracy: Understanding mistakes and uncovering biases, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11210 LNCS, pp. 504\u2013519, doi: 10.1007978-3-030-01231-1_31.","DOI":"10.1007\/978-3-030-01231-1_31"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_002_w2aab3b7b8b1b6b1ab1ab2Aa","unstructured":"[2] E. B. Kania, Chinese Military Innovation in Artificial Intelligence, 2019."},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_003_w2aab3b7b8b1b6b1ab1ab3Aa","doi-asserted-by":"crossref","unstructured":"[3] F. Wang, L. P. Casalino, and D. Khullar, Deep Learning in Medicine - Promise, Progress, and Challenges, JAMA Internal Medicine, vol. 179, no. 3. American Medical Association, pp. 293\u2013294, Mar. 01, 2019, doi: 10.1001\/jamaintern-med.2018.7117.","DOI":"10.1001\/jamainternmed.2018.7117"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_004_w2aab3b7b8b1b6b1ab1ab4Aa","doi-asserted-by":"crossref","unstructured":"[4] J. Folmsbee, S. Johnson, X. Liu, M. Brandwein-Weber, and S. Doyle, Fragile neural networks: the importance of image standardization for deep learning in digital pathology, in Medical Imaging 2019: Digital Pathology, Mar. 2019, vol. 10956, p. 38, doi: 10.1117\/12.2512992.10.1117\/12.2512992","DOI":"10.1117\/12.2512992"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_005_w2aab3b7b8b1b6b1ab1ab5Aa","doi-asserted-by":"crossref","unstructured":"[5] S. Lapuschkin, S. W\u00e4ldchen, A. Binder, G. Montavon, W. Samek, and K. R. M\u00fcller, Unmasking Clever Hans predictors and assessing what machines really learn, Nature Communications, vol. 10, no. 1, 2019, doi: 10.1038\/s41467-019-08987-4.10.1038\/s41467-019-08987-4641176930858366","DOI":"10.1038\/s41467-019-08987-4"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_006_w2aab3b7b8b1b6b1ab1ab6Aa","doi-asserted-by":"crossref","unstructured":"[6] R. M. J Byrne, Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning, 2019.10.24963\/ijcai.2019\/876","DOI":"10.24963\/ijcai.2019\/876"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_007_w2aab3b7b8b1b6b1ab1ab7Aa","unstructured":"[7] M. T. Ribeiro, S. Singh, and C. Guestrin, \u2018Why Should I Trust You?\u2019 Explaining the Predictions of Any Classifier, doi: 10.1145\/2939672.2939778.10.1145\/2939672.2939778"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_008_w2aab3b7b8b1b6b1ab1ab8Aa","unstructured":"[8] A. B. Arrieta et al., Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI, Oct. 2019"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_009_w2aab3b7b8b1b6b1ab1ab9Aa","doi-asserted-by":"crossref","unstructured":"[9] S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. M\u00fcller, and W. Samek, On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation, 2015, doi: 10.1371\/journal.pone.0130140.10.1371\/journal.pone.0130140449875326161953","DOI":"10.1371\/journal.pone.0130140"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_010_w2aab3b7b8b1b6b1ab1ac10Aa","unstructured":"[10] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, Grad-cam: Why did you say that? visual explanations from deep networks via gradient-based localization, Revista do Hospital das Clinicas, vol. 17, pp. 331\u2013336, 201610.1109\/ICCV.2017.74"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_011_w2aab3b7b8b1b6b1ab1ac11Aa","doi-asserted-by":"crossref","unstructured":"[11] S. Wachter, B. Mittelstadt, and C. Russell, COUNTERFACTUAL EXPLANATIONS WITHOUT OPENING THE BLACK BOX: AUTOMATED DECISIONS AND THE GDPR, Harvard Journal of Law & Technology, vol. 31, no. 2, 2018, doi: 10.1177\/1461444816676645.10.1177\/1461444816676645","DOI":"10.2139\/ssrn.3063289"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_012_w2aab3b7b8b1b6b1ab1ac12Aa","unstructured":"[12] W. Samek, T. Wiegand, and K.-R. M\u00fcller, Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models, Aug. 2017"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_013_w2aab3b7b8b1b6b1ab1ac13Aa","doi-asserted-by":"crossref","unstructured":"[13] J. Zhang, S. A. Bargal, Z. Lin, J. Brandt, X. Shen, and S. Sclaroff, Top-Down Neural Attention by Excitation Backprop, International Journal of Computer Vision, vol. 126, no. 10, pp. 1084\u20131102, Oct. 2018, doi: 10.1007\/s11263-017-1059-x.10.1007\/s11263-017-1059-x","DOI":"10.1007\/s11263-017-1059-x"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_014_w2aab3b7b8b1b6b1ab1ac14Aa","doi-asserted-by":"crossref","unstructured":"[14] G. Montavon, A. Binder, S. Lapuschkin, W. Samek, and K. R. M\u00fcller, Layer-Wise Relevance Propagation: An Overview, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11700 LNCS, Springer Verlag, 2019, pp. 193\u2013209.","DOI":"10.1007\/978-3-030-28954-6_10"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_015_w2aab3b7b8b1b6b1ab1ac15Aa","doi-asserted-by":"crossref","unstructured":"[15] W. Samek, A. Binder, G. Montavon, S. Lapuschkin, and K. R. M\u00fcller, Evaluating the visualization of what a deep neural network has learned, IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 11, pp. 2660\u20132673, 2017, doi: 10.1109\/TNNLS.2016.2599820.10.1109\/TNNLS.2016.259982027576267","DOI":"10.1109\/TNNLS.2016.2599820"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_016_w2aab3b7b8b1b6b1ab1ac16Aa","doi-asserted-by":"crossref","unstructured":"[16] A. Torralba and A. A. Efros, Unbiased look at dataset bias, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011, pp. 1521\u20131528, doi: 10.1109\/CVPR.2011.5995347.10.1109\/CVPR.2011.5995347","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_017_w2aab3b7b8b1b6b1ab1ac17Aa","doi-asserted-by":"crossref","unstructured":"[17] S. M. Lundberg et al., From local explanations to global understanding with explainable AI for trees, Nature Machine Intelligence, vol. 2, no. 1, pp. 56\u201367, Jan. 2020, doi: 10.1038\/s42256-019-0138-9.10.1038\/s42256-019-0138-9732636732607472","DOI":"10.1038\/s42256-019-0138-9"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_018_w2aab3b7b8b1b6b1ab1ac18Aa","unstructured":"[18] B. Kim et al., Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV), 2018."},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_019_w2aab3b7b8b1b6b1ab1ac19Aa","doi-asserted-by":"crossref","unstructured":"[19] G. Fidel, R. Bitton, and A. Shabtai, When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures, Sep. 201910.1109\/IJCNN48605.2020.9207637","DOI":"10.1109\/IJCNN48605.2020.9207637"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_020_w2aab3b7b8b1b6b1ab1ac20Aa","doi-asserted-by":"crossref","unstructured":"[20] A. M. \u0160imundi\u0107, Bias in research, Biochemia Medica, vol. 23, no. 1, pp. 12\u201315, Feb. 2013, doi: 10.11613\/BM.2013.003.10.11613\/BM.2013.003","DOI":"10.11613\/BM.2013.003"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_021_w2aab3b7b8b1b6b1ab1ac21Aa","doi-asserted-by":"crossref","unstructured":"[21] C. J. Pannucci and E. G. Wilkins, Identifying and avoiding bias in research, Plastic and Reconstructive Surgery, vol. 126, no. 2, pp. 619\u2013625, Aug. 2010, doi: 10.1097\/PRS.0b013e3181de24bc.10.1097\/PRS.0b013e3181de24bc291725520679844","DOI":"10.1097\/PRS.0b013e3181de24bc"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_022_w2aab3b7b8b1b6b1ab1ac22Aa","unstructured":"[22] R. Ambrosino, B. G. Buchanan, G. F. Cooper, and M. J. Fine, The use of misclassification costs to learn rule-based decision support models for cost-effective hospital admission strategies., Proceedings \/ the ... Annual Symposium on Computer Application [sic] in Medical Care. Symposium on Computer Applications in Medical Care, pp. 304\u2013308, 1995."},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_023_w2aab3b7b8b1b6b1ab1ac23Aa","doi-asserted-by":"crossref","unstructured":"[23] M. Thelwall, Gender bias in sentiment analysis, Online Information Review, vol. 42, no. 1, pp. 45\u201357, 2018, doi: 10.1108\/OIR-05-2017-0139.10.1108\/OIR-05-2017-0139","DOI":"10.1108\/OIR-05-2017-0139"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_024_w2aab3b7b8b1b6b1ab1ac24Aa","unstructured":"[24] P.-S. Huang et al., Reducing Sentiment Bias in Language Models via Counterfactual Evaluation, Nov. 201910.18653\/v1\/2020.findings-emnlp.7"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_025_w2aab3b7b8b1b6b1ab1ac25Aa","unstructured":"[25] M. Hardt Google, E. Price, and N. Srebro, Equality of Opportunity in Supervised Learning, 2016."},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_026_w2aab3b7b8b1b6b1ab1ac26Aa","doi-asserted-by":"crossref","unstructured":"[26] Jia Deng, Wei Dong, R. Socher, Li-Jia Li, Kai Li, and Li Fei-Fei, ImageNet: A large-scale hierarchical image database, in ieeexplore.ieee.org, 2009, pp. 248\u2013255, doi: 10.1109\/cvprw.2009.5206848.10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_027_w2aab3b7b8b1b6b1ab1ac27Aa","unstructured":"[27] R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel, ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness, 7th International Conference on Learning Representations, ICLR 2019, Nov. 2018"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_028_w2aab3b7b8b1b6b1ab1ac28Aa","doi-asserted-by":"crossref","unstructured":"[28] P. Tschandl, C. Rosendahl, and H. Kittler, The HAM10000 dataset, a large collection of multisource dermatoscopic images of common pigmented skin lesions, Scientific Data, vol. 5, Mar. 2018, doi: 10.1038\/sdata.2018.161.10.1038\/sdata.2018.161609124130106392","DOI":"10.1038\/sdata.2018.161"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_029_w2aab3b7b8b1b6b1ab1ac29Aa","unstructured":"[29] X. Sun, J. Yang, M. Sun, and K. Wang, A Benchmark for Automatic Visual Classification of Clinical Skin Disease Images."},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_030_w2aab3b7b8b1b6b1ab1ac30Aa","doi-asserted-by":"crossref","unstructured":"[30] A. Bissoto, M. Fornaciali, E. Valle, and S. Avila, (De)Constructing Bias on Skin Lesion Datasets, Apr. 201910.1109\/CVPRW.2019.00335","DOI":"10.1109\/CVPRW.2019.00335"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_031_w2aab3b7b8b1b6b1ab1ac31Aa","unstructured":"[31] C. Barata, J. S. Marques, and M. E. Celebi, Deep Attention Model for the Hierarchical Diagnosis of Skin Lesions."},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_032_w2aab3b7b8b1b6b1ab1ac32Aa","doi-asserted-by":"crossref","unstructured":"[32] B. Wang et al., Neural cleanse: Identifying and mitigating backdoor attacks in neural networks, in Proceedings - IEEE Symposium on Security and Privacy, May 2019, vol. 2019-May, pp. 707\u2013723, doi: 10.1109\/SP.2019.00031.10.1109\/SP.2019.00031","DOI":"10.1109\/SP.2019.00031"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_033_w2aab3b7b8b1b6b1ab1ac33Aa","unstructured":"[33] C. J. Anders, T. Marinc\u02c7, D. Neumann, W. Samek, K.-R. M\u00fcller, and S. Lapuschkin, Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans\u2019ed, Dec. 2019"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_034_w2aab3b7b8b1b6b1ab1ac34Aa","doi-asserted-by":"crossref","unstructured":"[34] A. Mikolajczyk and M. Grochowski, Style transfer-based image synthesis as an efficient regularization technique in deep learning, in 2019 24th International Conference on Methods and Models in Automation and Robotics, MMAR 2019, 2019, pp. 42\u201347, doi: 10.1109\/MMAR.2019.8864616.10.1109\/MMAR.2019.8864616","DOI":"10.1109\/MMAR.2019.8864616"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_035_w2aab3b7b8b1b6b1ab1ac35Aa","doi-asserted-by":"crossref","unstructured":"[35] G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, Densely Connected Convolutional Networks, Aug. 2016,10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_036_w2aab3b7b8b1b6b1ab1ac36Aa","doi-asserted-by":"crossref","unstructured":"[36] G. Montavon, S. Lapuschkin, A. Binder, W. Samek, and K. R. M\u00fcller, Explaining nonlinear classification decisions with deep Taylor decomposition, Pattern Recognition, vol. 65, pp. 211\u2013222, 2017, doi: 10.1016\/j.patcog.2016.11.008.10.1016\/j.patcog.2016.11.008","DOI":"10.1016\/j.patcog.2016.11.008"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_037_w2aab3b7b8b1b6b1ab1ac37Aa","doi-asserted-by":"crossref","unstructured":"[37] M. Balasubramanian, The Isomap Algorithm and Topological Stability, Science, vol. 295, no. 5552, pp. 7a \u2013 7, Jan. 2002, doi: 10.1126\/science.295.5552.7a.10.1126\/science.295.5552.7a","DOI":"10.1126\/science.295.5552.7a"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_038_w2aab3b7b8b1b6b1ab1ac38Aa","doi-asserted-by":"crossref","unstructured":"[38] D. Xu and Y. Tian, A Comprehensive Survey of Clustering Algorithms, Annals of Data Science, vol. 2, no. 2, pp. 165\u2013193, Jun. 2015, doi: 10.1007\/s40745-015-0040-1.10.1007\/s40745-015-0040-1","DOI":"10.1007\/s40745-015-0040-1"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_039_w2aab3b7b8b1b6b1ab1ac39Aa","unstructured":"[39] T. M. Kodinariya and P. R. Makwana, Review on determining number of Cluster in K-Means Clustering, International Journal of Advance Research in Computer Science and Management Studies, vol. 1, no. 6, 2013"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_040_w2aab3b7b8b1b6b1ab1ac40Aa","doi-asserted-by":"crossref","unstructured":"[40] J. Jaworek-Korjakowska, A Deep Learning Approach to Vascular Structure Segmentation in Dermoscopy Colour Images, hindawi.com, 2018, doi: 10.1155\/2018\/5049390.10.1155\/2018\/5049390","DOI":"10.1155\/2018\/5049390"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_041_w2aab3b7b8b1b6b1ab1ac41Aa","doi-asserted-by":"crossref","unstructured":"[41] R. H. Johr, Dermoscopy: Alternative melanocytic algorithms - The ABCD rule of dermatoscopy, menzies scoring method, and 7-point checklist, Clinics in Dermatology, vol. 20, no. 3, pp. 240\u2013247, May 2002, doi: 10.1016\/S0738-081X(02)00236-5.10.1016\/S0738-081X(02)00236-5","DOI":"10.1016\/S0738-081X(02)00236-5"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_042_w2aab3b7b8b1b6b1ab1ac42Aa","doi-asserted-by":"crossref","unstructured":"[42] T. Majtner, K. Lidayov\u00e1, S. Yildirim-Yayilgan, and J. Y. Hardeberg, Improving skin lesion segmentation in dermoscopic images by thin artefacts removal methods, Dec. 2016, doi: 10.1109\/EUVIP.2016.7764580.10.1109\/EUVIP.2016.7764580","DOI":"10.1109\/EUVIP.2016.7764580"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_043_w2aab3b7b8b1b6b1ab1ac43Aa","doi-asserted-by":"crossref","unstructured":"[43] A. Sultana, I. Dumitrache, M. Vocurek, and M. Ciuc, Removal of artifacts from dermatoscopic images, 2014, doi: 10.1109\/ICComm.2014.6866757.10.1109\/ICComm.2014.6866757","DOI":"10.1109\/ICComm.2014.6866757"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_044_w2aab3b7b8b1b6b1ab1ac44Aa","doi-asserted-by":"crossref","unstructured":"[44] M. E. Celebi, H. Iyatomi, G. Schaefer, and W. V. Stoecker, Lesion border detection in dermoscopy images, Computerized Medical Imaging and Graphics, vol. 33, no. 2, pp. 148\u2013153, Mar. 2009, doi: 10.1016\/j.compmedimag.2008.11.002.10.1016\/j.compmedimag.2008.11.002267119519121917","DOI":"10.1016\/j.compmedimag.2008.11.002"},{"key":"2026042813113387571_j_jaiscr-2021-0004_ref_045_w2aab3b7b8b1b6b1ab1ac45Aa","doi-asserted-by":"crossref","unstructured":"[45] C. Kim, K. Kim, and S. R. 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