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

[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. 504–519, doi: 10.1007978-3-030-01231-1_31.Search in Google Scholar

[2] E. B. Kania, Chinese Military Innovation in Artificial Intelligence, 2019.Search in Google Scholar

[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. 293–294, Mar. 01, 2019, doi: 10.1001/jamaintern-med.2018.7117.Search in Google Scholar

[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: 10.1117/12.2512992.10.1117/12.2512992Search in Google Scholar

[5] S. Lapuschkin, S. Wäldchen, A. Binder, G. Montavon, W. Samek, and K. R. Müller, 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/s41467-019-08987-4641176930858366Search in Google Scholar

[6] R. M. J Byrne, Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning, 2019.10.24963/ijcai.2019/876Search in Google Scholar

[7] M. T. Ribeiro, S. Singh, and C. Guestrin, ‘Why Should I Trust You?’ Explaining the Predictions of Any Classifier, doi: 10.1145/2939672.2939778.10.1145/2939672.2939778Search in Google Scholar

[8] A. B. Arrieta et al., Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI, Oct. 2019Search in Google Scholar

[9] S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, 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.0130140449875326161953Search in Google Scholar

[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. 331–336, 201610.1109/ICCV.2017.74Search in Google Scholar

[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: 10.1177/1461444816676645.10.1177/1461444816676645Search in Google Scholar

[12] W. Samek, T. Wiegand, and K.-R. Müller, Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models, Aug. 2017Search in Google Scholar

[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. 1084–1102, Oct. 2018, doi: 10.1007/s11263-017-1059-x.10.1007/s11263-017-1059-xSearch in Google Scholar

[14] G. Montavon, A. Binder, S. Lapuschkin, W. Samek, and K. R. Müller, 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. 193–209.Search in Google Scholar

[15] W. Samek, A. Binder, G. Montavon, S. Lapuschkin, and K. R. Müller, Evaluating the visualization of what a deep neural network has learned, IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 11, pp. 2660–2673, 2017, doi: 10.1109/TNNLS.2016.2599820.10.1109/TNNLS.2016.259982027576267Search in Google Scholar

[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. 1521–1528, doi: 10.1109/CVPR.2011.5995347.10.1109/CVPR.2011.5995347Search in Google Scholar

[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. 56–67, Jan. 2020, doi: 10.1038/s42256-019-0138-9.10.1038/s42256-019-0138-9732636732607472Search in Google Scholar

[18] B. Kim et al., Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV), 2018.Search in Google Scholar

[19] G. Fidel, R. Bitton, and A. Shabtai, When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures, Sep. 201910.1109/IJCNN48605.2020.9207637Search in Google Scholar

[20] A. M. Šimundić, Bias in research, Biochemia Medica, vol. 23, no. 1, pp. 12–15, Feb. 2013, doi: 10.11613/BM.2013.003.10.11613/BM.2013.003Search in Google Scholar

[21] C. J. Pannucci and E. G. Wilkins, Identifying and avoiding bias in research, Plastic and Reconstructive Surgery, vol. 126, no. 2, pp. 619–625, Aug. 2010, doi: 10.1097/PRS.0b013e3181de24bc.10.1097/PRS.0b013e3181de24bc291725520679844Search in Google Scholar

[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. 304–308, 1995.Search in Google Scholar

[23] M. Thelwall, Gender bias in sentiment analysis, Online Information Review, vol. 42, no. 1, pp. 45–57, 2018, doi: 10.1108/OIR-05-2017-0139.10.1108/OIR-05-2017-0139Search in Google Scholar

[24] P.-S. Huang et al., Reducing Sentiment Bias in Language Models via Counterfactual Evaluation, Nov. 201910.18653/v1/2020.findings-emnlp.7Search in Google Scholar

[25] M. Hardt Google, E. Price, and N. Srebro, Equality of Opportunity in Supervised Learning, 2016.Search in Google Scholar

[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. 248–255, doi: 10.1109/cvprw.2009.5206848.10.1109/CVPR.2009.5206848Search in Google Scholar

[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. 2018Search in Google Scholar

[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.2018.161609124130106392Search in Google Scholar

[29] X. Sun, J. Yang, M. Sun, and K. Wang, A Benchmark for Automatic Visual Classification of Clinical Skin Disease Images.Search in Google Scholar

[30] A. Bissoto, M. Fornaciali, E. Valle, and S. Avila, (De)Constructing Bias on Skin Lesion Datasets, Apr. 201910.1109/CVPRW.2019.00335Search in Google Scholar

[31] C. Barata, J. S. Marques, and M. E. Celebi, Deep Attention Model for the Hierarchical Diagnosis of Skin Lesions.Search in Google Scholar

[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. 707–723, doi: 10.1109/SP.2019.00031.10.1109/SP.2019.00031Search in Google Scholar

[33] C. J. Anders, T. Marincˇ, D. Neumann, W. Samek, K.-R. Müller, and S. Lapuschkin, Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans’ed, Dec. 2019Search in Google Scholar

[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. 42–47, doi: 10.1109/MMAR.2019.8864616.10.1109/MMAR.2019.8864616Search in Google Scholar

[35] G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, Densely Connected Convolutional Networks, Aug. 2016,10.1109/CVPR.2017.243Search in Google Scholar

[36] G. Montavon, S. Lapuschkin, A. Binder, W. Samek, and K. R. Müller, Explaining nonlinear classification decisions with deep Taylor decomposition, Pattern Recognition, vol. 65, pp. 211–222, 2017, doi: 10.1016/j.patcog.2016.11.008.10.1016/j.patcog.2016.11.008Search in Google Scholar

[37] M. Balasubramanian, The Isomap Algorithm and Topological Stability, Science, vol. 295, no. 5552, pp. 7a – 7, Jan. 2002, doi: 10.1126/science.295.5552.7a.10.1126/science.295.5552.7aSearch in Google Scholar

[38] D. Xu and Y. Tian, A Comprehensive Survey of Clustering Algorithms, Annals of Data Science, vol. 2, no. 2, pp. 165–193, Jun. 2015, doi: 10.1007/s40745-015-0040-1.10.1007/s40745-015-0040-1Search in Google Scholar

[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, 2013Search in Google Scholar

[40] J. Jaworek-Korjakowska, A Deep Learning Approach to Vascular Structure Segmentation in Dermoscopy Colour Images, hindawi.com, 2018, doi: 10.1155/2018/5049390.10.1155/2018/5049390Search in Google Scholar

[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. 240–247, May 2002, doi: 10.1016/S0738-081X(02)00236-5.10.1016/S0738-081X(02)00236-5Search in Google Scholar

[42] T. Majtner, K. Lidayová, 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.2016.7764580Search in Google Scholar

[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.2014.6866757Search in Google Scholar

[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. 148–153, Mar. 2009, doi: 10.1016/j.compmedimag.2008.11.002.10.1016/j.compmedimag.2008.11.002267119519121917Search in Google Scholar

[45] C. Kim, K. Kim, and S. R. Indurthi, Small energy masking for improved neural network training for end-to-end speech recognition, Feb. 2020,10.1109/ICASSP40776.2020.9054382Search in Google Scholar