{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T08:58:58Z","timestamp":1768726738709,"version":"3.49.0"},"reference-count":86,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T00:00:00Z","timestamp":1652227200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T00:00:00Z","timestamp":1652227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"SoBigData++","award":["871042"],"award-info":[{"award-number":["871042"]}]},{"name":"NoBias","award":["860630"],"award-info":[{"award-number":["860630"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>We present <jats:sc>xspells<\/jats:sc>, a model-agnostic local approach for explaining the decisions of black box models in classification of short texts. The explanations provided consist of a set of exemplar sentences and a set of counter-exemplar sentences. The former are examples classified by the black box with the same label as the text to explain. The latter are examples classified with a different label (a form of counter-factuals). Both are close in meaning to the text to explain, and both are meaningful sentences \u2013 albeit they are synthetically generated. <jats:sc>xspells<\/jats:sc> generates neighbors of the text to explain in a latent space using Variational Autoencoders for encoding text and decoding latent instances. A decision tree is learned from randomly generated neighbors, and used to drive the selection of the exemplars and counter-exemplars. Moreover, diversity of counter-exemplars is modeled as an optimization problem, solved by a greedy algorithm with theoretical guarantee. We report experiments on three datasets showing that <jats:sc>xspells<\/jats:sc> outperforms the well-known <jats:sc>lime<\/jats:sc> method in terms of quality of explanations, fidelity, diversity, and usefulness, and that is comparable to it in terms of stability.<\/jats:p>","DOI":"10.1007\/s10994-022-06150-7","type":"journal-article","created":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T17:13:47Z","timestamp":1652289227000},"page":"4289-4322","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Explaining short text classification with diverse synthetic exemplars and counter-exemplars"],"prefix":"10.1007","volume":"112","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8156-7281","authenticated-orcid":false,"given":"Orestis","family":"Lampridis","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8084-5297","authenticated-orcid":false,"given":"Laura","family":"State","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2827-7613","authenticated-orcid":false,"given":"Riccardo","family":"Guidotti","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1917-6087","authenticated-orcid":false,"given":"Salvatore","family":"Ruggieri","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,11]]},"reference":[{"key":"6150_CR1","doi-asserted-by":"crossref","unstructured":"T.\u00a0C. Alberto, J.\u00a0V. Lochter, and T.\u00a0A. Almeida. Tubespam: Comment spam filtering on youtube. In IEEE International Conference on Machine Learning and Applications (ICMLA 2015), pp 138\u2013143. IEEE, 2015.","DOI":"10.1109\/ICMLA.2015.37"},{"key":"6150_CR2","doi-asserted-by":"crossref","unstructured":"T.\u00a0Alhindi, S.\u00a0Petridis, and S.\u00a0Muresan. Where is your evidence: Improving fact-checking by justification modeling. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pp 85\u201390, Brussels, Belgium, 2018. Association for Computational Linguistics.","DOI":"10.18653\/v1\/W18-5513"},{"issue":"6","key":"6150_CR3","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.1016\/j.ipm.2018.08.001","volume":"54","author":"B Altinel","year":"2018","unstructured":"Altinel, B., & Ganiz, M. C. (2018). Semantic text classification: A survey of past and recent advances. Information Processing and Management, 54(6), 1129\u20131153.","journal-title":"Information Processing and Management"},{"key":"6150_CR4","unstructured":"D.\u00a0Alvarez-Melis and T.\u00a0S. Jaakkola. Towards robust interpretability with self-explaining neural networks. In Advances in Neural Information Processing Systems (NeurIPS 2018), pp 7786\u20137795, 2018."},{"issue":"8","key":"6150_CR5","doi-asserted-by":"publisher","first-page":"e0181142","DOI":"10.1371\/journal.pone.0181142","volume":"12","author":"L Arras","year":"2017","unstructured":"Arras, L., Horn, F., Montavon, G., M\u00fcller, K.-R., & Samek, W. (2017). What is relevant in a text document?: An interpretable machine learning approach. PLoS One, 12(8), e0181142.","journal-title":"PLoS One"},{"key":"6150_CR6","unstructured":"A.\u00a0Artelt and B.\u00a0Hammer. On the computation of counterfactual explanations \u2013 A survey. arXiv:1911.07749, 2019."},{"issue":"7","key":"6150_CR7","doi-asserted-by":"publisher","first-page":"e0130140","DOI":"10.1371\/journal.pone.0130140","volume":"10","author":"S Bach","year":"2015","unstructured":"Bach, S., Binder, A., Montavon, G., Klauschen, F., M\u00fcller, K.-R., & Samek, W. (2015). On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS one, 10(7), e0130140.","journal-title":"PloS one"},{"issue":"7","key":"6150_CR8","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1007\/s10994-017-5633-9","volume":"106","author":"D Bertsimas","year":"2017","unstructured":"Bertsimas, D., & Dunn, J. (2017). Optimal classification trees. Machine Learning, 106(7), 1039\u20131082.","journal-title":"Machine Learning"},{"key":"6150_CR9","unstructured":"F.\u00a0Bodria, F.\u00a0Giannotti, R.\u00a0Guidotti, F.\u00a0Naretto, D.\u00a0Pedreschi, and S.\u00a0Rinzivillo. Benchmarking and survey of explanation methods for black box models. CoRR, abs\/2102.13076, 2021."},{"key":"6150_CR10","unstructured":"T.\u00a0Bolukbasi, K.\u00a0Chang, J.\u00a0Y. Zou, V.\u00a0Saligrama, and A.\u00a0T. Kalai. Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In Advances in Neural Information Processing Systems (NIPS 2016), pp 4349\u20134357, 2016."},{"key":"6150_CR11","doi-asserted-by":"crossref","unstructured":"S.\u00a0R. Bowman, L.\u00a0Vilnis, O.\u00a0Vinyals, A.\u00a0M. Dai, R.\u00a0J\u00f3zefowicz, and S.\u00a0Bengio. Generating sentences from a continuous space. In Y.\u00a0Goldberg and S.\u00a0Riezler, editors, Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, CoNLL 2016, Berlin, Germany, 11-12, 2016, pp 10\u201321, 2016.","DOI":"10.18653\/v1\/K16-1002"},{"key":"6150_CR12","doi-asserted-by":"crossref","unstructured":"S.\u00a0R. Bowman, L.\u00a0Vilnis, O.\u00a0Vinyals, A.\u00a0M. Dai, R.\u00a0J\u00f3zefowicz, and S.\u00a0Bengio. Generating sentences from a continuous space. In Conference on Computational Natural Language Learning (CoNLL 2016), pp 10\u201321. ACL, 2016.","DOI":"10.18653\/v1\/K16-1002"},{"key":"6150_CR13","doi-asserted-by":"crossref","unstructured":"R.\u00a0M.\u00a0J. Byrne. Counterfactuals in explainable artificial intelligence (XAI): evidence from human reasoning. In Joint Conference on Artificial Intelligence (IJCAI 2019), pp 6276\u20136282. ijcai.org, 2019.","DOI":"10.24963\/ijcai.2019\/876"},{"key":"6150_CR14","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321\u2013357.","journal-title":"Journal of artificial intelligence research"},{"key":"6150_CR15","unstructured":"J.\u00a0Chen, L.\u00a0Song, M.\u00a0J. Wainwright, and M.\u00a0I. Jordan. Learning to explain: An information-theoretic perspective on model interpretation. In International Conference on Machine Learning, (ICML 2018), 80, pp 882\u2013891. PMLR, 2018."},{"key":"6150_CR16","doi-asserted-by":"crossref","unstructured":"J.\u00a0Clos and N.\u00a0Wiratunga. Lexicon induction for interpretable text classification. In J.\u00a0Kamps, G.\u00a0Tsakonas, Y.\u00a0Manolopoulos, L.\u00a0S. Iliadis, and I.\u00a0Karydis, editors, International Conference on Theory and Practice of Digital Libraries (TPDL 2017), 10450 of Lecture Notes in Computer Science, pp 498\u2013510. Springer, 2017.","DOI":"10.1007\/978-3-319-67008-9_39"},{"key":"6150_CR17","doi-asserted-by":"crossref","unstructured":"D.\u00a0Croce, D.\u00a0Rossini, and R.\u00a0Basili. Auditing deep learning processes through kernel-based explanatory models. In International Joint Conference on Natural Language Processing (AACL\/IJCNLP 2019), pp 4035\u20134044. ACL, 2019.","DOI":"10.18653\/v1\/D19-1415"},{"key":"6150_CR18","doi-asserted-by":"crossref","unstructured":"da Silva, N. F. F., Hruschka, E. R., & Jr, E. R. H. (2014). Tweet sentiment analysis with classifier ensembles. Decision support systems, 66, 170\u2013179.","DOI":"10.1016\/j.dss.2014.07.003"},{"key":"6150_CR19","doi-asserted-by":"crossref","unstructured":"F.\u00a0Dalvi, N.\u00a0Durrani, H.\u00a0Sajjad, Y.\u00a0Belinkov, A.\u00a0Bau, and J.\u00a0R. Glass. What is one grain of sand in the desert? Analyzing individual neurons in deep NLP models. In AAAI Conference on Artificial Intelligence (AAAI 2019), pp 6309\u20136317. AAAI Press, 2019.","DOI":"10.1609\/aaai.v33i01.33016309"},{"key":"6150_CR20","unstructured":"M.\u00a0Danilevsky, K.\u00a0Qian, R.\u00a0Aharonov, Y.\u00a0Katsis, B.\u00a0Kawas, and P.\u00a0Sen. A survey of the state of explainable AI for Natural Language Processing. In K.\u00a0Wong, K.\u00a0Knight, and H.\u00a0Wu, editors, International Joint Conference on Natural Language Processing (AACL\/IJCNLP 2020), pp 447\u2013459. ACL, 2020."},{"key":"6150_CR21","doi-asserted-by":"crossref","unstructured":"D.\u00a0Danks. The value of trustworthy AI. In AAAI\/ACM Conference on AI, Ethics, and Society (AIES 2019), pp 521\u2013522. ACM, 2019.","DOI":"10.1145\/3306618.3314228"},{"key":"6150_CR22","doi-asserted-by":"crossref","unstructured":"T.\u00a0Davidson, D.\u00a0Warmsley, M.\u00a0W. Macy, and I.\u00a0Weber. Automated hate speech detection and the problem of offensive language. In International Conference on Web and Social Media (ICWSM 2017), pp 512\u2013515. AAAI Press, 2017.","DOI":"10.1609\/icwsm.v11i1.14955"},{"key":"6150_CR23","unstructured":"J.\u00a0Devlin, M.\u00a0Chang, K.\u00a0Lee, and K.\u00a0Toutanova. BERT: pre-training of deep bidirectional transformers for language understanding. In Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, (NAACL-HLT 2019), pp 4171\u20134186. ACL, 2019."},{"key":"6150_CR24","unstructured":"F.\u00a0Doshi-Velez and B.\u00a0Kim. Towards a rigorous science of interpretable machine learning. arXiv:1702.08608, 2017."},{"key":"6150_CR25","doi-asserted-by":"crossref","unstructured":"U.\u00a0Ehsan and M.\u00a0O. Riedl. Human-centered explainable AI: towards a reflective sociotechnical approach. In C.\u00a0Stephanidis, M.\u00a0Kurosu, H.\u00a0Degen, and L.\u00a0Reinerman-Jones, editors, HCI International Conference (HCII 2020), 12424 of Lecture Notes in Computer Science, pp 449\u2013466. Springer, 2020.","DOI":"10.1007\/978-3-030-60117-1_33"},{"key":"6150_CR26","unstructured":"A.\u00a0Ene, S.\u00a0M. Nikolakaki, and E.\u00a0Terzi. Team formation: Striking a balance between coverage and cost. CoRR, abs\/2002.07782, 2020."},{"key":"6150_CR27","unstructured":"M.\u00a0F\u00f6rster, M.\u00a0Klier, K.\u00a0Kluge, and I.\u00a0Sigler. Evaluating explainable artifical intelligence - what users really appreciate. In European Conference on Information Systems (ECIS 2020), 2020."},{"issue":"1","key":"6150_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2594473.2594475","volume":"15","author":"AA Freitas","year":"2013","unstructured":"Freitas, A. A. (2013). Comprehensible classification models: A position paper. SIGKDD Explorations, 15(1), 1\u201310.","journal-title":"SIGKDD Explorations"},{"key":"6150_CR29","unstructured":"Y.\u00a0Goldberg and O.\u00a0Levy. word2vec explained: Deriving mikolov et al.\u2019s negative-sampling word-embedding method. CoRR, abs\/1402.3722, 2014."},{"issue":"6","key":"6150_CR30","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/MIS.2019.2957223","volume":"34","author":"R Guidotti","year":"2019","unstructured":"Guidotti, R., Monreale, A., Giannotti, F., Pedreschi, D., Ruggieri, S., & Turini, F. (2019). Factual and counterfactual explanations for black box decision making. IEEE Intelligent Systems, 34(6), 14\u201323.","journal-title":"IEEE Intelligent Systems"},{"issue":"5","key":"6150_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3236009","volume":"51","author":"R Guidotti","year":"2019","unstructured":"Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2019). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 1\u201342.","journal-title":"ACM Computing Surveys"},{"key":"6150_CR32","doi-asserted-by":"crossref","unstructured":"R.\u00a0Guidotti and S.\u00a0Ruggieri. On the stability of interpretable models. In International Joint Conference on Neural Networks (IJCNN 2019), pp 1\u20138. IEEE, 2019.","DOI":"10.1109\/IJCNN.2019.8852158"},{"key":"6150_CR33","unstructured":"C.\u00a0Harshaw, M.\u00a0Feldman, J.\u00a0Ward, and A.\u00a0Karbasi. Submodular maximization beyond non-negativity: Guarantees, fast algorithms, and applications. In International Conference on Machine Learning (ICML 2019), 97, pp 2634\u20132643. PMLR, 2019."},{"issue":"3","key":"6150_CR34","doi-asserted-by":"publisher","first-page":"1495","DOI":"10.1007\/s10462-017-9599-6","volume":"52","author":"F Hemmatian","year":"2019","unstructured":"Hemmatian, F., & Sohrabi, M. K. (2019). A survey on classification techniques for opinion mining and sentiment analysis. Artificial Intelligence Review, 52(3), 1495\u20131545.","journal-title":"Artificial Intelligence Review"},{"issue":"5786","key":"6150_CR35","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504\u2013507.","journal-title":"Science"},{"issue":"8","key":"6150_CR36","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735\u20131780.","journal-title":"Neural Computation"},{"key":"6150_CR37","doi-asserted-by":"crossref","unstructured":"B.\u00a0Hoover, H.\u00a0Strobelt, and S.\u00a0Gehrmann. exbert: A visual analysis tool to explore learned representations in transformers models. arXiv:1910.05276, 2019.","DOI":"10.18653\/v1\/2020.acl-demos.22"},{"key":"6150_CR38","unstructured":"B.\u00a0Kim, O.\u00a0Koyejo, and R.\u00a0Khanna. Examples are not enough, learn to criticize! criticism for interpretability. In Advances in Neural Information Processing Systems (NIPS 2016), pp 2280\u20132288, 2016."},{"key":"6150_CR39","unstructured":"D.\u00a0P. Kingma and M.\u00a0Welling. Auto-encoding variational Bayes. In International Conference on Learning Representations (ICLR 2014), 2014."},{"issue":"2","key":"6150_CR40","doi-asserted-by":"publisher","first-page":"85","DOI":"10.5121\/ijaia.2012.3208","volume":"3","author":"V Korde","year":"2012","unstructured":"Korde, V., & Mahender, C. N. (2012). Text classification and classifiers: A survey. International Journal of Artificial Intelligence & Applications, 3(2), 85.","journal-title":"International Journal of Artificial Intelligence & Applications"},{"issue":"4","key":"6150_CR41","doi-asserted-by":"publisher","first-page":"150","DOI":"10.3390\/info10040150","volume":"10","author":"K Kowsari","year":"2019","unstructured":"Kowsari, K., Meimandi, K. J., Heidarysafa, M., Mendu, S., Barnes, L. E., & Brown, D. E. (2019). Text classification algorithms: A survey. Information, 10(4), 150.","journal-title":"Information"},{"key":"6150_CR42","doi-asserted-by":"crossref","unstructured":"O.\u00a0Lampridis, R.\u00a0Guidotti, and S.\u00a0Ruggieri. Explaining sentiment classification with synthetic exemplars and counter-exemplars. In Discovery Science (DS 2020), 12323 of Lecture Notes in Computer Science, pp 357\u2013373. Springer, 2020.","DOI":"10.1007\/978-3-030-61527-7_24"},{"key":"6150_CR43","doi-asserted-by":"crossref","unstructured":"C.\u00a0Li, X.\u00a0Gao, Y.\u00a0Li, B.\u00a0Peng, X.\u00a0Li, Y.\u00a0Zhang, and J.\u00a0Gao. Optimus: Organizing sentences via pre-trained modeling of a latent space. In Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), pp 4678\u20134699. ACL, 2020.","DOI":"10.18653\/v1\/2020.emnlp-main.378"},{"key":"6150_CR44","unstructured":"J.\u00a0Li, W.\u00a0Monroe, and D.\u00a0Jurafsky. Understanding neural networks through representation erasure. arXiv:1612.08220, 2016."},{"key":"6150_CR45","unstructured":"Q.\u00a0Li, H.\u00a0Peng, J.\u00a0Li, C.\u00a0Xia, R.\u00a0Yang, L.\u00a0Sun, P.\u00a0S. Yu, and L.\u00a0He. A survey on text classification: From shallow to deep learning. CoRR, abs\/2008.00364, 2020."},{"key":"6150_CR46","doi-asserted-by":"crossref","unstructured":"X.\u00a0Li and D.\u00a0Roth. Learning question classifiers. In COLING 2002: The 19th International Conference on Computational Linguistics, 2002.","DOI":"10.3115\/1072228.1072378"},{"issue":"1","key":"6150_CR47","doi-asserted-by":"publisher","first-page":"18","DOI":"10.3390\/e23010018","volume":"23","author":"P Linardatos","year":"2021","unstructured":"Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2021). Explainable AI: A review of machine learning interpretability methods. Entropy, 23(1), 18.","journal-title":"Entropy"},{"key":"6150_CR48","doi-asserted-by":"crossref","unstructured":"B.\u00a0Liu and L.\u00a0Zhang. A survey of opinion mining and sentiment analysis. In Mining Text Data, pp 415\u2013463. Springer, 2012.","DOI":"10.1007\/978-1-4614-3223-4_13"},{"key":"6150_CR49","unstructured":"S.\u00a0M. Lundberg and S.\u00a0Lee. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems (NIPS 2017), pp 4765\u20134774, 2017."},{"issue":"4","key":"6150_CR50","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1093\/idpl\/ipx019","volume":"7","author":"G Malgieri","year":"2017","unstructured":"Malgieri, G., & Comand\u00e9, G. (2017). Why a right to legibility of automated decision-making exists in the GDPR. International Data Privacy Law, 7(4), 243\u2013265.","journal-title":"International Data Privacy Law"},{"key":"6150_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artint.2018.07.007","volume":"267","author":"T Miller","year":"2019","unstructured":"Miller, T. (2019). Explanation in Artificial Intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1\u201338.","journal-title":"Artificial Intelligence"},{"key":"6150_CR52","doi-asserted-by":"crossref","unstructured":"Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., & Gao, J. (2021). Deep learning-based text classification: A comprehensive review. ACM Computing Surveys, 54(3), 1\u201340.","DOI":"10.1145\/3439726"},{"key":"6150_CR53","doi-asserted-by":"crossref","unstructured":"M.\u00a0Minoux. Accelerated greedy algorithms for maximizing submodular set functions. In Optimization Techniques, pp 234\u2013243. Springer, 1978.","DOI":"10.1007\/BFb0006528"},{"key":"6150_CR54","doi-asserted-by":"crossref","unstructured":"I.\u00a0Mollas, N.\u00a0Bassiliades, and G.\u00a0Tsoumakas. Lionets: Local interpretation of neural networks through penultimate layer decoding. In Machine Learning and Knowledge Discovery in Databases \u2013 Workshops (ECML-PKDD 2019), pp 265\u2013276. Springer, 2019.","DOI":"10.1007\/978-3-030-43823-4_23"},{"key":"6150_CR55","doi-asserted-by":"crossref","unstructured":"R.\u00a0K. Mothilal, A.\u00a0Sharma, and C.\u00a0Tan. Explaining machine learning classifiers through diverse counterfactual explanations. In Conference on Fairness, Accountability, and Transparency (FAT* 2020), pp 607\u2013617. ACM, 2020.","DOI":"10.1145\/3351095.3372850"},{"issue":"1","key":"6150_CR56","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/BF01588971","volume":"14","author":"GL Nemhauser","year":"1978","unstructured":"Nemhauser, G. L., Wolsey, L. A., & Fisher, M. L. (1978). An analysis of approximations for maximizing submodular set functions - I. Mathematical Programming, 14(1), 265\u2013294.","journal-title":"Mathematical Programming"},{"key":"6150_CR57","doi-asserted-by":"crossref","unstructured":"D.\u00a0Nguyen. Comparing automatic and human evaluation of local explanations for text classification. In Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, (NAACL-HLT 2018), pp 1069\u20131078. ACL, 2018.","DOI":"10.18653\/v1\/N18-1097"},{"issue":"3","key":"6150_CR58","first-page":"e1356","volume":"10","author":"E Ntoutsi","year":"2020","unstructured":"Ntoutsi, E., et al. (2020). Bias in data-driven Artificial Intelligence systems - An introductory survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1356.","journal-title":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"},{"key":"6150_CR59","doi-asserted-by":"publisher","first-page":"13","DOI":"10.3389\/fdata.2019.00013","volume":"2","author":"A Olteanu","year":"2019","unstructured":"Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2019). Social data: Biases, methodological pitfalls, and ethical boundaries. Frontiers Big Data, 2, 13.","journal-title":"Frontiers Big Data"},{"key":"6150_CR60","doi-asserted-by":"crossref","unstructured":"B.\u00a0Pang and L.\u00a0Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Annual Meeting of the Association for Computational Linguistics (ACL 2005), pp 115\u2013124. ACL, 2005.","DOI":"10.3115\/1219840.1219855"},{"key":"6150_CR61","doi-asserted-by":"crossref","unstructured":"D.\u00a0Pedreschi, F.\u00a0Giannotti, R.\u00a0Guidotti, A.\u00a0Monreale, S.\u00a0Ruggieri, and F.\u00a0Turini. Meaningful explanations of black box AI decision systems. In AAAI Conference on Artificial Intelligence (AAAI 2019), pp 9780\u20139784. AAAI Press, 2019.","DOI":"10.1609\/aaai.v33i01.33019780"},{"key":"6150_CR62","doi-asserted-by":"crossref","unstructured":"K.\u00a0Qian, M.\u00a0Danilevsky, Y.\u00a0Katsis, B.\u00a0Kawas, E.\u00a0Oduor, L.\u00a0Popa, and Y.\u00a0Li. XNLP: A living survey for XAI research in Natural Language Processing. In International Conference on Intelligent User Interfaces (IUI 2021), pp 78\u201380. ACM, 2021.","DOI":"10.1145\/3397482.3450728"},{"key":"6150_CR63","doi-asserted-by":"crossref","unstructured":"M.\u00a0T. Ribeiro, S.\u00a0Singh, and C.\u00a0Guestrin. \u201cWhy should I trust you?\": Explaining the predictions of any classifier. In ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD 2016), pp 1135\u20131144. ACM, 2016.","DOI":"10.18653\/v1\/N16-3020"},{"key":"6150_CR64","doi-asserted-by":"crossref","unstructured":"M.\u00a0T. Ribeiro, S.\u00a0Singh, and C.\u00a0Guestrin. Anchors: High-precision model-agnostic explanations. In AAAI Conference on Artificial Intelligence (AAAI 2018), pages 1527\u20131535. AAAI Press, 2018.","DOI":"10.1609\/aaai.v32i1.11491"},{"issue":"5","key":"6150_CR65","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206\u2013215.","journal-title":"Nature Machine Intelligence"},{"key":"6150_CR66","doi-asserted-by":"crossref","unstructured":"S.\u00a0Ruggieri. Subtree replacement in decision tree simplification. In International Conference on Data Mining (SDM 2012), pp 379\u2013390. SIAM, 2012.","DOI":"10.1137\/1.9781611972825.33"},{"key":"6150_CR67","first-page":"104:1","volume":"20","author":"S Ruggieri","year":"2019","unstructured":"Ruggieri, S. (2019). Complete search for feature selection in decision trees. J. Mach. Learn. Res., 20, 104:1-104:34.","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"6150_CR68","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/505282.505283","volume":"34","author":"F Sebastiani","year":"2002","unstructured":"Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys, 34(1), 1\u201347.","journal-title":"ACM Computing Surveys"},{"issue":"4","key":"6150_CR69","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1093\/idpl\/ipx022","volume":"7","author":"AD Selbst","year":"2017","unstructured":"Selbst, A. D., & Powles, J. (2017). Meaningful information and the right to explanation. International Data Privacy Law, 7(4), 233\u2013242.","journal-title":"International Data Privacy Law"},{"key":"6150_CR70","doi-asserted-by":"crossref","unstructured":"S.\u00a0M. Shankaranarayana and D.\u00a0Runje. Alime: Autoencoder based approach for local interpretability. In Intelligent Data Engineering and Automated Learning (IDEAL), 11871 of Lecture Notes in Computer Science, pp 454\u2013463. Springer, 2019.","DOI":"10.1007\/978-3-030-33607-3_49"},{"key":"6150_CR71","unstructured":"A.\u00a0Shrikumar, P.\u00a0Greenside, and A.\u00a0Kundaje. Learning important features through propagating activation differences. In International Conference on Machine Learning (ICML 2017), pp 3145\u20133153. PMLR, 2017."},{"key":"6150_CR72","doi-asserted-by":"publisher","first-page":"101104","DOI":"10.1016\/j.csl.2020.101104","volume":"65","author":"B Skrlj","year":"2021","unstructured":"Skrlj, B., Martinc, M., Kralj, J., Lavrac, N., & Pollak, S. (2021). tax2vec: Constructing interpretable features from taxonomies for short text classification. Computer Speech & Language, 65, 101104.","journal-title":"Computer Speech & Language"},{"issue":"5","key":"6150_CR73","doi-asserted-by":"publisher","first-page":"635","DOI":"10.4304\/jmm.9.5.635-643","volume":"9","author":"G Song","year":"2014","unstructured":"Song, G., Ye, Y., Du, X., Huang, X., & Bie, S. (2014). Short text classification: A survey. Journal of Multimedia, 9(5), 635\u2013643.","journal-title":"Journal of Multimedia"},{"key":"6150_CR74","unstructured":"M.\u00a0Sundararajan, A.\u00a0Taly, and Q.\u00a0Yan. Axiomatic attribution for deep networks. In International Conference on Machine Learning (ICML 2017), pp 3319\u20133328. PMLR, 2017."},{"key":"6150_CR75","unstructured":"I.\u00a0Sutskever, O.\u00a0Vinyals, and Q.\u00a0V. Le. Sequence to sequence learning with neural networks. In NIPS, pp 3104\u20133112, 2014."},{"key":"6150_CR76","unstructured":"P.-N. Tan, M.\u00a0Steinbach, and V.\u00a0Kumar. Introduction to Data Mining. Pearson Education India, 2016."},{"key":"6150_CR77","unstructured":"A.\u00a0Vaswani, N.\u00a0Shazeer, N.\u00a0Parmar, J.\u00a0Uszkoreit, L.\u00a0Jones, A.\u00a0N. Gomez, L.\u00a0Kaiser, and I.\u00a0Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems (NIPS 2017), pp 5998\u20136008, 2017."},{"key":"6150_CR78","unstructured":"S.\u00a0Verma, J.\u00a0P. Dickerson, and K.\u00a0Hines. Counterfactual explanations for machine learning: A review. CoRR, abs\/2010.10596, 2020."},{"key":"6150_CR79","doi-asserted-by":"crossref","unstructured":"G.\u00a0Visani, E.\u00a0Bagli, F.\u00a0Chesani, A.\u00a0Poluzzi, and D.\u00a0Capuzzo. Statistical stability indices for lime: Obtaining reliable explanations for machine learning models. Journal of the Operational Research Society, page to appear, 2021.","DOI":"10.1080\/01605682.2020.1865846"},{"key":"6150_CR80","doi-asserted-by":"crossref","unstructured":"W.\u00a0Y. Wang. \"liar, liar pants on fire\": A new benchmark dataset for fake news detection. In ACL (2), pp 422\u2013426. Association for Computational Linguistics, 2017.","DOI":"10.18653\/v1\/P17-2067"},{"key":"6150_CR81","unstructured":"S.\u00a0Wiegreffe and A.\u00a0Marasovi\u0107. Teach me to explain: A review of datasets for explainable NLP. arXiv:2102.12060 0, 2021."},{"issue":"12","key":"6150_CR82","doi-asserted-by":"publisher","first-page":"2913","DOI":"10.4304\/jcp.7.12.2913-2920","volume":"7","author":"B Xu","year":"2012","unstructured":"Xu, B., Guo, X., Ye, Y., & Cheng, J. (2012). An improved random forest classifier for text categorization. J. Comput., 7(12), 2913\u20132920.","journal-title":"J. Comput."},{"issue":"3","key":"6150_CR83","doi-asserted-by":"publisher","first-page":"525","DOI":"10.3390\/make3030027","volume":"3","author":"MR Zafar","year":"2021","unstructured":"Zafar, M. R., & Khan, N. (2021). Deterministic local interpretable model-agnostic explanations for stable explainability. Machine Learning and Knowledge Extraction, 3(3), 525\u2013541.","journal-title":"Machine Learning and Knowledge Extraction"},{"key":"6150_CR84","unstructured":"X.\u00a0Zhang, J.\u00a0J. Zhao, and Y.\u00a0LeCun. Character-level convolutional networks for text classification. In Advances in Neural Information Processing Systems (NIPS 2015), pp 649\u2013657, 2015."},{"issue":"3","key":"6150_CR85","doi-asserted-by":"publisher","first-page":"205","DOI":"10.3233\/WEB-200442","volume":"18","author":"X Zhou","year":"2020","unstructured":"Zhou, X., Gururajan, R., Li, Y., Venkataraman, R., Tao, X., Bargshady, G., Barua, P. D., & Kondalsamy-Chennakesavan, S. (2020). A survey on text classification and its applications. Web Intelligence, 18(3), 205\u2013216.","journal-title":"Web Intelligence"},{"key":"6150_CR86","doi-asserted-by":"crossref","unstructured":"Z.\u00a0Zhou, G.\u00a0Hooker, and F.\u00a0Wang. S-lime: Stabilized-lime for model explanation. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021), pp 2429\u20132438. ACM, 2021.","DOI":"10.1145\/3447548.3467274"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06150-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-022-06150-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06150-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T17:12:09Z","timestamp":1698253929000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-022-06150-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,11]]},"references-count":86,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["6150"],"URL":"https:\/\/doi.org\/10.1007\/s10994-022-06150-7","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,11]]},"assertion":[{"value":"1 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 August 2022","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Missing Open Access funding information has been added in the Funding Note.","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}]}}