{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T23:48:35Z","timestamp":1767138515351,"version":"build-2238731810"},"publisher-location":"Cham","reference-count":82,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031440663","type":"print"},{"value":"9783031440670","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    In the field of Computer Vision (CV), the degree to which two objects, e.g. two classes, share a common conceptual meaning, known as semantic similarity, is closely linked to the visual resemblance of their physical appearances in the data: entities with higher semantic similarity, typically exhibit greater visual resemblance than entities with lower semantic similarity. Deep Neural Networks (DNNs) employed for classification exploit this visual similarity, incorporating it into the network\u2019s representations (e.g., neurons), resulting in the functional similarity between the learned representations of visually akin classes, often manifesting in correlated activation patterns. However, such functional similarities can also emerge from spurious correlations \u2014 undesired auxiliary features that are shared between classes, such as backgrounds or specific artifacts. In this work, we present the\n                    <jats:italic>Function-Semantic Contrast Analysis<\/jats:italic>\n                    (FSCA) method, which identifies potential unintended correlations between network representations by examining the contrast between the functional distance of representations and the knowledge-based semantic distance between the concepts these representations were trained to recognize. While natural discrepancy is expected, our results indicate that these differences often originate from harmful spurious correlations in the data. We validate our approach by examining the presence of spurious correlations in widely-used CV architectures, demonstrating that FSCA offers a scalable solution for discovering previously undiscovered biases, that reduces the need for human supervision and is applicable across various Image Classification problems.\n                  <\/jats:p>","DOI":"10.1007\/978-3-031-44067-0_28","type":"book-chapter","created":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T02:02:33Z","timestamp":1697767353000},"page":"549-572","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Finding Spurious Correlations with\u00a0Function-Semantic Contrast Analysis"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3358-2858","authenticated-orcid":false,"given":"Kirill","family":"Bykov","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4280-3818","authenticated-orcid":false,"given":"Laura","family":"Kopf","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3090-6279","authenticated-orcid":false,"given":"Marina M.-C.","family":"H\u00f6hne","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"28_CR1","unstructured":"Adebayo, J., Muelly, M., Abelson, H., Kim, B.: Post hoc explanations may be ineffective for detecting unknown spurious correlation. In: International Conference on Learning Representations (2022)"},{"key":"28_CR2","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.inffus.2021.07.015","volume":"77","author":"CJ Anders","year":"2022","unstructured":"Anders, C.J., Weber, L., Neumann, D., Samek, W., M\u00fcller, K.R., Lapuschkin, S.: Finding and removing clever hans: using explanation methods to debug and improve deep model. Inf. Fusion 77, 261\u2013295 (2022)","journal-title":"Inf. Fusion"},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Bach, S., Binder, A., Montovon, G., Klauschen, F., M\u00fcller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10(7), e0130140 (2015)","DOI":"10.1371\/journal.pone.0130140"},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Bau, D., Zhou, B., Khosla, A., Oliva, A., Torralba, A.: Network dissection: quantifying interpretability of deep visual representations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6541\u20136549 (2017)","DOI":"10.1109\/CVPR.2017.354"},{"key":"28_CR5","unstructured":"Bau, D., et al.: GAN dissection: visualizing and understanding generative adversarial networks. arXiv preprint arXiv:1811.10597 (2018)"},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Beery, S., Van Horn, G., Perona, P.: Recognition in terra incognita. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 456\u2013473 (2018)","DOI":"10.1007\/978-3-030-01270-0_28"},{"issue":"8","key":"28_CR7","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798\u20131828 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"28_CR8","doi-asserted-by":"crossref","unstructured":"Bianchi, F., et al.: Easily accessible text-to-image generation amplifies demographic stereotypes at large scale (2022)","DOI":"10.1145\/3593013.3594095"},{"issue":"1","key":"28_CR9","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1109\/TVCG.2017.2744683","volume":"24","author":"A Bilal","year":"2017","unstructured":"Bilal, A., Jourabloo, A., Ye, M., Liu, X., Ren, L.: Do convolutional neural networks learn class hierarchy? IEEE Trans. Visual Comput. Graphics 24(1), 152\u2013162 (2017)","journal-title":"IEEE Trans. Visual Comput. Graphics"},{"key":"28_CR10","doi-asserted-by":"crossref","unstructured":"Bissoto, A., Valle, E., Avila, S.: Debiasing skin lesion datasets and models? Not so fast. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 740\u2013741 (2020)","DOI":"10.1109\/CVPRW50498.2020.00378"},{"key":"28_CR11","unstructured":"Borowski, J., et al.: Natural images are more informative for interpreting CNN activations than state-of-the-art synthetic feature visualizations. In: NeurIPS 2020 Workshop SVRHM (2020)"},{"key":"28_CR12","unstructured":"Brendel, W., Bethge, M.: Approximating CNNs with bag-of-local-features models works surprisingly well on ImageNet (2019)"},{"key":"28_CR13","doi-asserted-by":"crossref","unstructured":"Brown, K.E., Talbert, D.A.: Using explainable AI to measure feature contribution to uncertainty. In: The International FLAIRS Conference Proceedings, vol. 35 (2022)","DOI":"10.32473\/flairs.v35i.130662"},{"key":"28_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1007\/978-3-030-33676-9_29","volume-title":"Pattern Recognition","author":"C-A Brust","year":"2019","unstructured":"Brust, C.-A., Denzler, J.: Not just a matter of semantics: the relationship between visual and semantic similarity. In: Fink, G.A., Frintrop, S., Jiang, X. (eds.) DAGM GCPR 2019. LNCS, vol. 11824, pp. 414\u2013427. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-33676-9_29"},{"issue":"4","key":"28_CR15","doi-asserted-by":"publisher","first-page":"966","DOI":"10.3390\/make3040048","volume":"3","author":"V Buhrmester","year":"2021","unstructured":"Buhrmester, V., M\u00fcnch, D., Arens, M.: Analysis of explainers of black box deep neural networks for computer vision: a survey. Mach. Learn. Knowl. Extract. 3(4), 966\u2013989 (2021)","journal-title":"Mach. Learn. Knowl. Extract."},{"key":"28_CR16","unstructured":"Buolamwini, J., Gebru, T.: Gender shades: intersectional accuracy disparities in commercial gender classification. In: Proceedings of the 1st Conference on Fairness, Accountability and Transparency, pp. 77\u201391. PMLR (2018)"},{"key":"28_CR17","unstructured":"Bykov, K., Deb, M., Grinwald, D., M\u00fcller, K.R., H\u00f6hne, M.M.C.: DORA: exploring outlier representations in deep neural networks. arXiv preprint arXiv:2206.04530 (2022)"},{"key":"28_CR18","doi-asserted-by":"crossref","unstructured":"Bykov, K., Hedstr\u00f6m, A., Nakajima, S., H\u00f6hne, M.M.C.: NoiseGrad-enhancing explanations by introducing stochasticity to model weights. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 6132\u20136140 (2022)","DOI":"10.1609\/aaai.v36i6.20561"},{"key":"28_CR19","unstructured":"Bykov, K., et al.: Explaining Bayesian neural networks. arXiv preprint arXiv:2108.10346 (2021)"},{"key":"28_CR20","doi-asserted-by":"crossref","unstructured":"Bykov, K., M\u00fcller, K.R., H\u00f6hne, M.M.C.: Mark my words: dangers of watermarked images in ImageNet (2023)","DOI":"10.1007\/978-3-031-50396-2_24"},{"key":"28_CR21","first-page":"100134","volume":"6","author":"J Chai","year":"2021","unstructured":"Chai, J., Zeng, H., Li, A., Ngai, E.W.: Deep learning in computer vision: a critical review of emerging techniques and application scenarios. Mach. Learn. Appl. 6, 100134 (2021)","journal-title":"Mach. Learn. Appl."},{"key":"28_CR22","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"28_CR23","doi-asserted-by":"crossref","unstructured":"Deselaers, T., Ferrari, V.: Visual and semantic similarity in Imagenet. In: CVPR 2011, pp. 1777\u20131784. IEEE (2011)","DOI":"10.1109\/CVPR.2011.5995474"},{"key":"28_CR24","unstructured":"Dosovitskiy, A., et al.: An image is worth 16$$\\times $$16 words: transformers for image recognition at scale. In: ICLR (2021)"},{"key":"28_CR25","unstructured":"Erhan, D., Bengio, Y., Courville, A., Vincent, P.: Visualizing higher-layer features of a deep network. Technical report, Univerist\u00e9 de Montr\u00e9al (2009)"},{"key":"28_CR26","unstructured":"Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL visual object classes challenge 2007 (VOC2007) results. http:\/\/www.pascal-network.org\/challenges\/VOC\/voc2007\/workshop\/index.html"},{"key":"28_CR27","doi-asserted-by":"crossref","unstructured":"Gade, K., Geyik, S.C., Kenthapadi, K., Mithal, V., Taly, A.: Explainable AI in industry. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, pp. 3203\u20133204. Association for Computing Machinery, New York (2019)","DOI":"10.1145\/3292500.3332281"},{"key":"28_CR28","doi-asserted-by":"crossref","unstructured":"Gautam, S., H\u00f6hne, M.M.C., Hansen, S., Jenssen, R., Kampffmeyer, M.: Demonstrating the risk of imbalanced datasets in chest X-ray image-based diagnostics by prototypical relevance propagation. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1\u20135. IEEE (2022)","DOI":"10.1109\/ISBI52829.2022.9761651"},{"key":"28_CR29","doi-asserted-by":"crossref","unstructured":"Ghosal, S.S., Ming, Y., Li, Y.: Are vision transformers robust to spurious correlations? (2022)","DOI":"10.1007\/s11263-023-01916-5"},{"key":"28_CR30","doi-asserted-by":"publisher","first-page":"103428","DOI":"10.1016\/j.artint.2020.103428","volume":"291","author":"R Guidotti","year":"2021","unstructured":"Guidotti, R.: Evaluating local explanation methods on ground truth. Artif. Intell. 291, 103428 (2021)","journal-title":"Artif. Intell."},{"issue":"2","key":"28_CR31","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1207\/S15328031US0202_03","volume":"2","author":"BD Haig","year":"2003","unstructured":"Haig, B.D.: What Is a spurious correlation? Underst. Stat.: Stat. Issues Psycho. Educ. Soc. Sci. 2(2), 125\u2013132 (2003)","journal-title":"Underst. Stat.: Stat. Issues Psycho. Educ. Soc. Sci."},{"issue":"1","key":"28_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-031-02156-5","volume":"8","author":"S Harispe","year":"2015","unstructured":"Harispe, S., Ranwez, S., Janaqi, S., Montmain, J.: Semantic similarity from natural language and ontology analysis. Synthesis Lect. Hum. Lang. Technol. 8(1), 1\u2013254 (2015)","journal-title":"Synthesis Lect. Hum. Lang. Technol."},{"key":"28_CR33","unstructured":"Hedstr\u00f6m, A., Bommer, P., Wickstr\u00f8m, K.K., Samek, W., Lapuschkin, S., H\u00f6hne, M.M.C.: The meta-evaluation problem in explainable AI: identifying reliable estimators with MetaQuantus. arXiv preprint arXiv:2302.07265 (2023)"},{"key":"28_CR34","unstructured":"Hedstr\u00f6m, A., et al.: Quantus: an explainable AI toolkit for responsible evaluation of neural network explanations and beyond. arXiv preprint arXiv:2202.06861 (2022)"},{"key":"28_CR35","unstructured":"Hernandez, E., Schwettmann, S., Bau, D., Bagashvili, T., Torralba, A., Andreas, J.: Natural language descriptions of deep visual features. In: International Conference on Learning Representations (2021)"},{"key":"28_CR36","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/978-3-031-04083-2_2","volume-title":"xxAI - Beyond Explainable AI","author":"A Holzinger","year":"2022","unstructured":"Holzinger, A., Saranti, A., Molnar, C., Biecek, P., Samek, W.: Explainable AI methods - a brief overview. In: Holzinger, A., Goebel, R., Fong, R., Moon, T., M\u00fcller, K.R., Samek, W. (eds.) xxAI 2020. LNCS, vol. 13200, pp. 13\u201338. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-04083-2_2"},{"key":"28_CR37","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"28_CR38","doi-asserted-by":"publisher","first-page":"103459","DOI":"10.1016\/j.artint.2021.103459","volume":"294","author":"EM Kenny","year":"2021","unstructured":"Kenny, E.M., Ford, C., Quinn, M., Keane, M.T.: Explaining black-box classifiers using post-hoc explanations-by-example: the effect of explanations and error-rates in XAI user studies. Artif. Intell. 294, 103459 (2021)","journal-title":"Artif. Intell."},{"key":"28_CR39","unstructured":"Kirillov, A., et al.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)"},{"key":"28_CR40","doi-asserted-by":"crossref","unstructured":"Kolesnikov, A., Lampert, C.H.: Improving weakly-supervised object localization by micro-annotation (2016)","DOI":"10.5244\/C.30.92"},{"key":"28_CR41","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images. University of Toronto (2009)"},{"issue":"1","key":"28_CR42","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1038\/s41467-019-08987-4","volume":"10","author":"S Lapuschkin","year":"2019","unstructured":"Lapuschkin, S., W\u00e4ldchen, S., Binder, A., Montavon, G., Samek, W., M\u00fcller, K.R.: Unmasking Clever Hans predictors and assessing what machines really learn. Nat. Commun. 10(1), 1096 (2019)","journal-title":"Nat. Commun."},{"key":"28_CR43","doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: A Whac-a-mole dilemma: shortcuts come in multiples where mitigating one amplifies others. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20071\u201320082 (2023)","DOI":"10.1109\/CVPR52729.2023.01922"},{"key":"28_CR44","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"28_CR45","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"28_CR46","doi-asserted-by":"crossref","unstructured":"Mann, H.B., Whitney, D.R.: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 50\u201360 (1947)","DOI":"10.1214\/aoms\/1177730491"},{"key":"28_CR47","doi-asserted-by":"crossref","unstructured":"Marcel, S., Rodriguez, Y.: Torchvision the machine-vision package of torch. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 1485\u20131488 (2010)","DOI":"10.1145\/1873951.1874254"},{"issue":"11","key":"28_CR48","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/219717.219748","volume":"38","author":"GA Miller","year":"1995","unstructured":"Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39\u201341 (1995)","journal-title":"Commun. ACM"},{"key":"28_CR49","unstructured":"Minh, D., Wang, H.X., Li, Y.F., Nguyen, T.N.: Explainable artificial intelligence: a comprehensive review. Artif. Intell. Rev. 1\u201366 (2022)"},{"key":"28_CR50","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1016\/j.neucom.2022.01.005","volume":"493","author":"Y Mo","year":"2022","unstructured":"Mo, Y., Wu, Y., Yang, X., Liu, F., Liao, Y.: Review the state-of-the-art technologies of semantic segmentation based on deep learning. Neurocomputing 493, 626\u2013646 (2022)","journal-title":"Neurocomputing"},{"key":"28_CR51","doi-asserted-by":"crossref","unstructured":"Morbidelli, P., Carrera, D., Rossi, B., Fragneto, P., Boracchi, G.: Augmented Grad-CAM: heat-maps super resolution through augmentation. In: ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4067\u20134071. IEEE (2020)","DOI":"10.1109\/ICASSP40776.2020.9054416"},{"key":"28_CR52","unstructured":"Mu, J., Andreas, J.: Compositional explanations of neurons. In: Advances in Neural Information Processing Systems, vol. 33, pp. 17153\u201317163 (2020)"},{"key":"28_CR53","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/978-3-030-28954-6_4","volume-title":"Explainable AI: Interpreting, Explaining and Visualizing Deep Learning","author":"A Nguyen","year":"2019","unstructured":"Nguyen, A., Yosinski, J., Clune, J.: Understanding neural networks via feature visualization: a survey. In: Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., M\u00fcller, K.-R. (eds.) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. LNCS (LNAI), vol. 11700, pp. 55\u201376. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-28954-6_4"},{"key":"28_CR54","doi-asserted-by":"crossref","unstructured":"Nguyen, A.M., Yosinski, J., Clune, J.: Innovation engines: automated creativity and improved stochastic optimization via deep learning. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 959\u2013966 (2015)","DOI":"10.1145\/2739480.2754703"},{"issue":"11","key":"28_CR55","doi-asserted-by":"publisher","DOI":"10.23915\/distill.00007","volume":"2","author":"C Olah","year":"2017","unstructured":"Olah, C., Mordvintsev, A., Schubert, L.: Feature visualization. Distill 2(11), e7 (2017)","journal-title":"Distill"},{"key":"28_CR56","doi-asserted-by":"crossref","unstructured":"Pedersen, T., Patwardhan, S., Michelizzi, J., et al.: WordNet::similarity-measuring the relatedness of concepts. In: AAAI, vol. 4, pp. 25\u201329 (2004)","DOI":"10.3115\/1614025.1614037"},{"key":"28_CR57","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: Proceedings of the 38th International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"key":"28_CR58","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cWhy should i trust you?\": explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"28_CR59","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"28_CR60","unstructured":"Sagawa, S., Koh, P.W., Hashimoto, T.B., Liang, P.: Distributionally robust neural networks for group shifts: on the importance of regularization for worst-case generalization (2020)"},{"issue":"3","key":"28_CR61","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1109\/JPROC.2021.3060483","volume":"109","author":"W Samek","year":"2021","unstructured":"Samek, W., Montavon, G., Lapuschkin, S., Anders, C.J., M\u00fcller, K.R.: Explaining deep neural networks and beyond: a review of methods and applications. Proc. IEEE 109(3), 247\u2013278 (2021)","journal-title":"Proc. IEEE"},{"key":"28_CR62","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6","volume-title":"Explainable AI: Interpreting, Explaining and Visualizing Deep Learning","author":"W Samek","year":"2019","unstructured":"Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., M\u00fcller, K.R.: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, vol. 11700. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-28954-6"},{"key":"28_CR63","unstructured":"Scriver, A.: Semantic distance in WordNet: a simplified and improved measure of semantic relatedness. Master\u2019s thesis, University of Waterloo (2006)"},{"issue":"2","key":"28_CR64","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2019","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vision 128(2), 336\u2013359 (2019)","journal-title":"Int. J. Comput. Vision"},{"issue":"12","key":"28_CR65","doi-asserted-by":"publisher","first-page":"2176","DOI":"10.1038\/s41591-021-01595-0","volume":"27","author":"L Seyyed-Kalantari","year":"2021","unstructured":"Seyyed-Kalantari, L., Zhang, H., McDermott, M.B.A., Chen, I.Y., Ghassemi, M.: Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat. Med. 27(12), 2176\u20132182 (2021)","journal-title":"Nat. Med."},{"key":"28_CR66","doi-asserted-by":"crossref","unstructured":"Shetty, R., Schiele, B., Fritz, M.: Not using the car to see the sidewalk - quantifying and controlling the effects of context in classification and segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8218\u20138226 (2019)","DOI":"10.1109\/CVPR.2019.00841"},{"key":"28_CR67","doi-asserted-by":"publisher","first-page":"102551","DOI":"10.1016\/j.ijhcs.2020.102551","volume":"146","author":"D Shin","year":"2021","unstructured":"Shin, D.: The effects of explainability and causability on perception, trust, and acceptance: implications for explainable AI. Int. J. Hum Comput Stud. 146, 102551 (2021)","journal-title":"Int. J. Hum Comput Stud."},{"issue":"267","key":"28_CR68","first-page":"467","volume":"49","author":"HA Simon","year":"1954","unstructured":"Simon, H.A.: Spurious correlation: a causal interpretation. J. Am. Stat. Assoc. 49(267), 467\u2013479 (1954)","journal-title":"J. Am. Stat. Assoc."},{"key":"28_CR69","unstructured":"Singla, S., Feizi, S.: Salient ImageNet: how to discover spurious features in deep learning? In: International Conference on Learning Representations (2022)"},{"key":"28_CR70","unstructured":"Slack, D., Hilgard, A., Singh, S., Lakkaraju, H.: Reliable post hoc explanations: modeling uncertainty in explainability. In: Advances in Neural Information Processing Systems, vol. 34, pp. 9391\u20139404 (2021)"},{"key":"28_CR71","unstructured":"Smilkov, D., Thorat, N., Kim, B., Vi\u00e9gas, F., Wattenberg, M.: SmoothGrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825 (2017)"},{"key":"28_CR72","doi-asserted-by":"crossref","unstructured":"Stock, P., Cisse, M.: ConvNets and ImageNet beyond accuracy: understanding mistakes and uncovering biases. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 498\u2013512 (2018)","DOI":"10.1007\/978-3-030-01231-1_31"},{"key":"28_CR73","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: International Conference on Machine Learning, pp. 3319\u20133328. PMLR (2017)"},{"key":"28_CR74","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"11","key":"28_CR75","doi-asserted-by":"publisher","first-page":"4793","DOI":"10.1109\/TNNLS.2020.3027314","volume":"32","author":"E Tjoa","year":"2020","unstructured":"Tjoa, E., Guan, C.: A survey on explainable artificial intelligence (XAI): toward medical XAI. IEEE Trans. Neural Netw. Learn. Syst. 32(11), 4793\u20134813 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"28_CR76","doi-asserted-by":"publisher","first-page":"102368","DOI":"10.1016\/j.media.2022.102368","volume":"77","author":"D Wallis","year":"2022","unstructured":"Wallis, D., Buvat, I.: Clever Hans effect found in a widely used brain tumour MRI dataset. Med. Image Anal. 77, 102368 (2022)","journal-title":"Med. Image Anal."},{"key":"28_CR77","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.neucom.2020.01.085","volume":"396","author":"X Wu","year":"2020","unstructured":"Wu, X., Sahoo, D., Hoi, S.C.: Recent advances in deep learning for object detection. Neurocomputing 396, 39\u201364 (2020)","journal-title":"Neurocomputing"},{"key":"28_CR78","unstructured":"Xiao, K., Engstrom, L., Ilyas, A., Madry, A.: Noise or signal: the role of image backgrounds in object recognition (2020)"},{"key":"28_CR79","unstructured":"Yu, J., Wang, Z., Vasudevan, V., Yeung, L., Seyedhosseini, M., Wu, Y.: CoCa: contrastive captioners are image-text foundation models. arXiv preprint arXiv:2205.01917 (2022)"},{"key":"28_CR80","doi-asserted-by":"crossref","unstructured":"Zaidi, S.S.A., Ansari, M.S., Aslam, A., Kanwal, N., Asghar, M., Lee, B.: A survey of modern deep learning based object detection models. Digit. Signal Process. 103514 (2022)","DOI":"10.1016\/j.dsp.2022.103514"},{"issue":"11","key":"28_CR81","doi-asserted-by":"publisher","first-page":"e1002683","DOI":"10.1371\/journal.pmed.1002683","volume":"15","author":"JR Zech","year":"2018","unstructured":"Zech, J.R., Badgeley, M.A., Liu, M., Costa, A.B., Titano, J.J., Oermann, E.K.: Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med. 15(11), e1002683 (2018)","journal-title":"PLoS Med."},{"key":"28_CR82","doi-asserted-by":"crossref","unstructured":"Zhao, J., Wang, T., Yatskar, M., Ordonez, V., Chang, K.W.: Men also like shopping: reducing gender bias amplification using corpus-level constraints. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2979\u20132989. Association for Computational Linguistics, Copenhagen (2017)","DOI":"10.18653\/v1\/D17-1323"}],"updated-by":[{"DOI":"10.1007\/978-3-031-44067-0_33","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2024,2,14]],"date-time":"2024-02-14T00:00:00Z","timestamp":1707868800000}}],"container-title":["Communications in Computer and Information Science","Explainable Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44067-0_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T01:08:39Z","timestamp":1707786519000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44067-0_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031440663","9783031440670"],"references-count":82,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44067-0_28","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"21 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"14 February 2024","order":2,"name":"change_date","label":"Change Date","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Correction","order":3,"name":"change_type","label":"Change Type","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"A correction has been published.","order":4,"name":"change_details","label":"Change Details","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"xAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"World Conference on Explainable Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lisbon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"xai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/xaiworldconference.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"220","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"94","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"43% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}