{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:53:17Z","timestamp":1743036797793,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031606137"},{"type":"electronic","value":"9783031606113"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-60611-3_28","type":"book-chapter","created":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T01:06:47Z","timestamp":1717204007000},"page":"407-423","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Through the\u00a0Eyes of\u00a0the\u00a0Expert: Aligning Human and\u00a0Machine Attention for\u00a0Industrial AI"],"prefix":"10.1007","author":[{"given":"Alexander","family":"Koebler","sequence":"first","affiliation":[]},{"given":"Christian","family":"Greisinger","sequence":"additional","affiliation":[]},{"given":"Jan","family":"Paulus","sequence":"additional","affiliation":[]},{"given":"Ingo","family":"Thon","sequence":"additional","affiliation":[]},{"given":"Florian","family":"Buettner","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,1]]},"reference":[{"key":"28_CR1","unstructured":"Antunes, C., Silveira, M.: Generating attention maps from eye-gaze for the diagnosis of Alzheimer\u2019s disease. In: NeuRIPS 2022 Workshop on Gaze Meets ML (2022). https:\/\/openreview.net\/forum?id=yL1qcv2Q0bC"},{"key":"28_CR2","doi-asserted-by":"publisher","unstructured":"Barrett, M., Bingel, J., Hollenstein, N., Rei, M., S\u00f8gaard, A.: Sequence classification with human attention. In: Korhonen, A., Titov, I. (eds.) Proceedings of the 22nd Conference on Computational Natural Language Learning, pp. 302\u2013312. Association for Computational Linguistics, Brussels, Belgium, October 2018. https:\/\/doi.org\/10.18653\/v1\/K18-1030, https:\/\/aclanthology.org\/K18-1030","DOI":"10.18653\/v1\/K18-1030"},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Bhatt, U., Weller, A., Moura, J.M.: Evaluating and aggregating feature-based model explanations. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 3016\u20133022 (2021)","DOI":"10.24963\/ijcai.2020\/417"},{"key":"28_CR4","unstructured":"Bisla, D., Choromanska, A.: VisualBackProp for learning using privileged information with CNNs. Technical Report arXiv:1805.09474, arXiv, May 2018, http:\/\/arxiv.org\/abs\/1805.09474, arXiv:1805.09474 [cs] type: article"},{"key":"28_CR5","doi-asserted-by":"publisher","unstructured":"Decker, T., Gross, R., Koebler, A., Lebacher, M., Schnitzer, R., Weber, S.H.: The thousand faces of explainable AI along the machine learning life cycle: industrial reality and current state of research. In: Degen, H., Ntoa, S. (eds.) Artificial Intelligence in HCI. HCII 2023. Lecture Notes in Computer Science, Part I, vol. 14050, pp 184\u2013208. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-35891-3_13","DOI":"10.1007\/978-3-031-35891-3_13"},{"key":"28_CR6","doi-asserted-by":"publisher","unstructured":"Geirhos, R., et al.: shortcut learning in deep neural networks. Nat. Mach. Intell. 2(11), 665\u2013673 (2020)https:\/\/doi.org\/10.1038\/s42256-020-00257-z, http:\/\/arxiv.org\/abs\/2004.07780, arXiv:2004.07780 [cs, q-bio]","DOI":"10.1038\/s42256-020-00257-z"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"28_CR8","unstructured":"Hedstr\u00f6m, A., et al.: Quantus: an explainable AI toolkit for responsible evaluation of neural network explanations and beyond. J. Mach. Learn. Res. 24(34), 1\u201311 (2023). http:\/\/jmlr.org\/papers\/v24\/22-0142.html"},{"key":"28_CR9","unstructured":"Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. In: International Conference on Learning Representations (2018)"},{"key":"28_CR10","doi-asserted-by":"publisher","unstructured":"Hollenstein, N., Zhang, C.: Entity recognition at first sight: improving NER with eye movement information. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1\u201310. Association for Computational Linguistics, Minneapolis, Minnesota, June 2019. https:\/\/doi.org\/10.18653\/v1\/N19-1001, https:\/\/aclanthology.org\/N19-1001","DOI":"10.18653\/v1\/N19-1001"},{"key":"28_CR11","unstructured":"Kauffmann, J., Ruff, L., Montavon, G., M\u00fcller, K.R.: The clever hans effect in anomaly detection. arXiv:2006.10609 [cs, stat], June 2020. http:\/\/arxiv.org\/abs\/2006.10609, arXiv: 2006.10609"},{"key":"28_CR12","unstructured":"Koebler, A., Decker, T., Lebacher, M., Thon, I., Tresp, V., Buettner, F.: Towards explanatory model monitoring. In: XAI in Action: Past, Present, and Future Applications (2023). https:\/\/openreview.net\/forum?id=nVGuWh4S2G"},{"key":"28_CR13","doi-asserted-by":"crossref","unstructured":"Kohlbrenner, M., Bauer, A., Nakajima, S., Binder, A., Samek, W., Lapuschkin, S.: Towards best practice in explaining neural network decisions with LRP. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20137. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9206975"},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"Lambert, J., Sener, O., Savarese, S.: Deep learning under privileged information using heteroscedastic dropout. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8886\u20138895 (2018)","DOI":"10.1109\/CVPR.2018.00926"},{"key":"28_CR15","doi-asserted-by":"publisher","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). https:\/\/doi.org\/10.1038\/s41467-019-08987-4, https:\/\/www.nature.com\/articles\/s41467-019-08987-4","DOI":"10.1038\/s41467-019-08987-4"},{"key":"28_CR16","doi-asserted-by":"publisher","first-page":"3384","DOI":"10.1109\/TMI.2023.3287572","volume":"42","author":"C Ma","year":"2023","unstructured":"Ma, C., et al.: Eye-gaze-guided vision transformer for rectifying shortcut learning. IEEE Trans. Med. Imaging 42, 3384\u20133394 (2023)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"28_CR17","unstructured":"Read, J., Perez-Cruz, F.: Deep learning for multi-label classification. arXiv preprint arXiv:1502.05988 (2014)"},{"key":"28_CR18","doi-asserted-by":"crossref","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211\u2013252 (2015)","DOI":"10.1007\/s11263-015-0816-y"},{"key":"28_CR19","unstructured":"Saab, K., Dunnmon, J., Ratner, A., Rubin, D., Re, C.: Improving sample complexity with observational supervision (2019). https:\/\/openreview.net\/forum?id=r1gPtjcH_N"},{"key":"28_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1007\/978-3-030-87196-3_56","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"K Saab","year":"2021","unstructured":"Saab, K., et al.: Observational supervision for medical image classification using gaze data. In: de Bruijne, M., et al. (eds.) MICCAI 2021, Part II. LNCS, vol. 12902, pp. 603\u2013614. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87196-3_56"},{"key":"28_CR21","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1007\/978-3-642-23808-6_10","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"K Sechidis","year":"2011","unstructured":"Sechidis, K., Tsoumakas, G., Vlahavas, I.: On the stratification of multi-label data. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part III. LNCS (LNAI), vol. 6913, pp. 145\u2013158. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-23808-6_10"},{"key":"28_CR22","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7-9 May 2015, Conference Track Proceedings (2015), http:\/\/arxiv.org\/abs\/1409.1556"},{"key":"28_CR23","unstructured":"Sucholutsky, I., Griffiths, T.L.: Alignment with human representations supports robust few-shot learning. In: Thirty-seventh Conference on Neural Information Processing Systems (2023). https:\/\/openreview.net\/forum?id=HYGnmSLBCf"},{"key":"28_CR24","unstructured":"Vapnik, V., Izmailov, R.: Learning using privileged information: similarity control and knowledge transfer. J. Mach. Learn. Res. 16(61), 2023\u20132049 (2015). http:\/\/jmlr.org\/papers\/v16\/vapnik15b.html"},{"key":"28_CR25","doi-asserted-by":"publisher","unstructured":"Vapnik, V., Vashist, A.: A new learning paradigm: learning using privileged information. Neural Netw. 22(5), 544\u2013557 (2009). https:\/\/doi.org\/10.1016\/j.neunet.2009.06.042 , https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0893608009001130","DOI":"10.1016\/j.neunet.2009.06.042"},{"key":"28_CR26","doi-asserted-by":"crossref","unstructured":"Von\u00a0Rueden, L., et al.: Informed machine learning-a taxonomy and survey of integrating prior knowledge into learning systems. IEEE Trans. Knowl. Data Eng. 35(1), 614\u2013633 (2021)","DOI":"10.1109\/TKDE.2021.3079836"},{"key":"28_CR27","doi-asserted-by":"publisher","unstructured":"Wang, J., Ma, Y., Zhang, L., Gao, R.X., Wu, D.: Deep learning for smart manufacturing: methods and applications. J. Manuf. Syst. 48, 144\u2013156 (2018). https:\/\/doi.org\/10.1016\/j.jmsy.2018.01.003, https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0278612518300037","DOI":"10.1016\/j.jmsy.2018.01.003"},{"key":"28_CR28","doi-asserted-by":"crossref","unstructured":"Wang, S., Ouyang, X., Liu, T., Wang, Q., Shen, D.: Follow my eye: using gaze to supervise computer-aided diagnosis. IEEE Trans. Med. Imaging 41, 1688\u20131698 (2022). https:\/\/api.semanticscholar.org\/CorpusID:246359652","DOI":"10.1109\/TMI.2022.3146973"},{"key":"28_CR29","doi-asserted-by":"crossref","unstructured":"Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics Bull. 1(6), 80\u201383 (1945). http:\/\/www.jstor.org\/stable\/3001968","DOI":"10.2307\/3001968"},{"key":"28_CR30","doi-asserted-by":"publisher","unstructured":"Wuest, T., Weimer, D., Irgens, C., Thoben, K.D.: Machine learning in manufacturing: advantages, challenges, and applications. Prod. Manuf. Res. 4, 23\u201345 (2016). https:\/\/doi.org\/10.1080\/21693277.2016.1192517","DOI":"10.1080\/21693277.2016.1192517"},{"key":"28_CR31","doi-asserted-by":"publisher","unstructured":"Yun, K., Peng, Y., Samaras, D., Zelinsky, G., Berg, T.: Exploring the role of gaze behavior and object detection in scene understanding. Front. Psychol. 4 (2013). https:\/\/doi.org\/10.3389\/fpsyg.2013.00917, https:\/\/www.frontiersin.org\/articles\/10.3389\/fpsyg.2013.00917","DOI":"10.3389\/fpsyg.2013.00917"},{"key":"28_CR32","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in HCI"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-60611-3_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T01:21:36Z","timestamp":1717204896000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-60611-3_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031606137","9783031606113"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-60611-3_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Washington DC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}