{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:36:10Z","timestamp":1784738170762,"version":"3.55.0"},"reference-count":296,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior\u2013Brasil (CAPES)\u2013Finance Code 001"},{"DOI":"10.13039\/501100001807","name":"S\u00e3o Paulo Research Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100005993","name":"National Council for Scientific and Technological Development","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005993","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["2018\/24516-0"],"award-info":[{"award-number":["2018\/24516-0"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["2022\/09091-8"],"award-info":[{"award-number":["2022\/09091-8"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["307184\/2021-8"],"award-info":[{"award-number":["307184\/2021-8"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3409843","type":"journal-article","created":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T17:57:40Z","timestamp":1717610260000},"page":"80799-80846","source":"Crossref","is-referenced-by-count":100,"title":["EXplainable Artificial Intelligence (XAI)\u2014From Theory to Methods and Applications"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1459-9643","authenticated-orcid":false,"given":"Evandro S.","family":"Ortigossa","sequence":"first","affiliation":[{"name":"Institute of Mathematics and Computer Science, University of São Paulo (ICMC-USP), São Carlos, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8190-943X","authenticated-orcid":false,"given":"Thales","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"Institute of Mathematics and Computer Science, University of São Paulo (ICMC-USP), São Carlos, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8514-8033","authenticated-orcid":false,"given":"Luis Gustavo","family":"Nonato","sequence":"additional","affiliation":[{"name":"Institute of Mathematics and Computer Science, University of São Paulo (ICMC-USP), São Carlos, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","volume-title":"Artificial Intelligence: A Modern Approach","author":"Russell","year":"2010"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.12.012"},{"key":"ref3","volume-title":"The Future of Work: Robots, AI, and Automation","author":"West","year":"2018"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2870052"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1900654116"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3229161"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.23919\/MIPRO.2018.8400040"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1631\/FITEE.1700808"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/UIC-ATC.2017.8397411"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1126\/science.aau6595"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-018-0304-0"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3236386.3241340"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0138-9"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-55524-9_21"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000016"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1177\/2053951715622512"},{"key":"ref19","article-title":"Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models","author":"Samek","year":"2017","journal-title":"arXiv:1708.08296"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2788613"},{"key":"ref21","article-title":"Towards a rigorous science of interpretable machine learning","author":"Doshi-Velez","year":"2017","journal-title":"arXiv:1702.08608"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2018.07.007"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3301275.3308446"},{"key":"ref24","volume-title":"2016\/679 of the European Parliament and of the council of 27 April 2016 on the General Data Protection Regulation","year":"2023"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.479"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3236009"},{"key":"ref27","volume-title":"Proposed Regulatory Framework for Modifications to Artificial Intelligence\/Machine Learning (AI\/ML)-Based Software as a Medical Device (SaMD)\u2014Discussion Paper and Request for Feedback","year":"2024"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102306"},{"key":"ref29","volume-title":"Ethics and Governance of Artificial Intelligence for Health: WHO Guidance","year":"2021"},{"key":"ref30","volume-title":"California Consumer Privacy Act (CCPA)","year":"2021"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2018.00018"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.3390\/e23010018"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-023-06335-8"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2021.688969"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.12228"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.491"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3387166"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.05.009"},{"key":"ref39","first-page":"235","article-title":"A survey on methods and metrics for the assessment of explainability under the proposed AI act","volume-title":"Legal Knowledge and Information Systems","volume":"346","author":"Vitali","year":"2022"},{"key":"ref40","volume-title":"Interpretable Machine Learning","author":"Molnar","year":"2019"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3534639"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3027314"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.cjca.2021.09.004"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12020237"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102470"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020190043"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.07.016"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107161"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1038\/s41576-022-00532-2"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3052449"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3122865"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-022-01657-x"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3146552"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1038\/s41524-022-00884-7"},{"key":"ref55","article-title":"A survey of the state of explainable AI for natural language processing","author":"Danilevsky","year":"2020","journal-title":"arXiv:2010.00711"},{"key":"ref56","volume-title":"Computational Intelligence: A Logical Approach","author":"Poole","year":"1998"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/j.cobeha.2018.12.010"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/0167-2789(90)90087-6"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1147\/rd.33.0210"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-12405-5_1"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1016\/b978-0-12-809715-1.00012-2"},{"key":"ref62","volume-title":"The Neural Network Zoo\u2014The Asimov Institute","year":"2016"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref64","article-title":"The theory behind overfitting, cross validation, regularization, bagging, and boosting: Tutorial","author":"Ghojogh","year":"2019","journal-title":"arXiv:1905.12787"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1145\/1412700.1412710"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804090"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1016\/j.cognition.2010.10.004"},{"key":"ref69","volume-title":"Accurately Measuring Model Prediction Error","author":"Fortmann-Roe","year":"2012"},{"key":"ref70","article-title":"A modern take on the bias-variance tradeoff in neural networks","author":"Neal","year":"2018","journal-title":"arXiv:1810.08591"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref72","volume-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"ref73","volume-title":"Understanding the Bias-Variance Tradeoff","author":"Fortmann-Roe","year":"2012"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1992.4.1.1"},{"key":"ref75","article-title":"Reconstructing training data from trained neural networks","author":"Haim","year":"2022","journal-title":"arXiv:2206.07758"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1145\/3446776"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1907378117"},{"key":"ref78","first-page":"24164","article-title":"Benign overfitting in multiclass classification: All roads lead to interpolation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Wang"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.2919024"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-43505-2_14"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1214\/07-AOAS148"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.2307\/2699986"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.2014.907095"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-10925-7_40"},{"key":"ref85","volume-title":"Noise: A Flaw in Human Judgment","author":"Kahneman","year":"2021"},{"key":"ref86","first-page":"1","article-title":"Shapley residuals: Quantifying the limits of the Shapley value for explanations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Kumar"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1145\/3274405"},{"key":"ref88","article-title":"What does explainable AI really mean? A new conceptualization of perspectives","author":"Doran","year":"2017","journal-title":"arXiv:1710.00794"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1016\/j.tics.2006.08.004"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3375624"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1089\/big.2016.0007"},{"key":"ref92","volume-title":"An Introduction to Statistical Learning: With Applications in R","volume":"1","author":"Gareth","year":"2017"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1145\/1007730.1007735"},{"key":"ref94","article-title":"Attention interpretability across NLP tasks","author":"Vashishth","year":"2019","journal-title":"arXiv:1909.11218"},{"key":"ref95","article-title":"Attention is not explanation","author":"Jain","year":"2019","journal-title":"arXiv:1902.10186"},{"key":"ref96","volume-title":"XKCD\u2014A Webcomic of Romance, Sarcasm, Math, and Language","author":"Munroe","year":"2023"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-3020"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2016.2598838"},{"key":"ref99","article-title":"Interpreting neural networks using flip points","author":"Yousefzadeh","year":"2019","journal-title":"arXiv:1903.08789"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1002"},{"key":"ref101","article-title":"Characterizing the decision boundary of deep neural networks","author":"Karimi","year":"2019","journal-title":"arXiv:1912.11460"},{"key":"ref102","first-page":"1","article-title":"Do deep nets really need to be deep?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"27","author":"Ba"},{"key":"ref103","article-title":"Intriguing properties of neural networks","author":"Szegedy","year":"2013","journal-title":"arXiv:1312.6199"},{"key":"ref104","article-title":"Explaining and harnessing adversarial examples","author":"Goodfellow","year":"2014","journal-title":"arXiv:1412.6572"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2019.2890858"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-08987-4"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-98131-4_2"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445315"},{"key":"ref109","first-page":"4768","article-title":"A unified approach to interpreting model predictions","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Lundberg"},{"key":"ref110","article-title":"Benchmarking attribution methods with relative feature importance","author":"Yang","year":"2019","journal-title":"arXiv:1907.09701"},{"issue":"4","key":"ref111","first-page":"1","article-title":"The need for user models in generating expert system explanations","volume":"1","author":"Kass","year":"1988","journal-title":"Int. J. Exp. Syst."},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487579"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-53352-6_24"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-31384-3"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-006-0120-x"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2017.09.029"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2021.103502"},{"key":"ref118","article-title":"Melody: Generating and visualizing machine learning model summary to understand data and classifiers together","author":"Yeuk-Yin Chan","year":"2020","journal-title":"arXiv:2007.10614"},{"key":"ref119","article-title":"Is Shapley explanation for a model unique?","author":"Kumar","year":"2021","journal-title":"arXiv:2111.11946"},{"key":"ref120","article-title":"SUBPLEX: Towards a better understanding of black box model explanations at the subpopulation level","author":"Yuan","year":"2020","journal-title":"arXiv:2007.10609"},{"key":"ref121","first-page":"5105","article-title":"Feature importance ranking for deep learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Wojtas"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3051315"},{"key":"ref123","article-title":"On the robustness of interpretability methods","author":"Alvarez-Melis","year":"2018","journal-title":"arXiv:1806.08049"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376590"},{"key":"ref125","article-title":"A survey of privacy-preserving model explanations: Privacy risks, attacks, and countermeasures","author":"Tam Nguyen","year":"2024","journal-title":"arXiv:2404.00673"},{"key":"ref126","article-title":"Opportunities and challenges in explainable artificial intelligence (XAI): A survey","author":"Das","year":"2020","journal-title":"arXiv:2006.11371"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1145\/3457607"},{"key":"ref128","article-title":"A survey on the robustness of feature importance and counterfactual explanations","author":"Mishra","year":"2021","journal-title":"arXiv:2111.00358"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN55064.2022.9892946"},{"key":"ref130","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2018.2846735"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1503.02531"},{"key":"ref133","article-title":"Local rule-based explanations of black box decision systems","author":"Guidotti","year":"2018","journal-title":"arXiv:1805.10820"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1007\/s13347-019-00382-7"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339556"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.1201\/9780203753781"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.403"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1016\/j.apr.2019.03.001"},{"key":"ref139","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2017.2744718"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.3390\/app13031595"},{"key":"ref141","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2018.2843369"},{"key":"ref142","article-title":"Model-agnostic interpretation by visualization of feature perturbations","author":"Marc\u00edlio-","year":"2021","journal-title":"arXiv:2101.10502"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.1080\/07350015.2019.1624293"},{"key":"ref144","first-page":"322","article-title":"GALE: Globally assessing local explanations","volume-title":"Proc. ICML","author":"Xenopoulos"},{"key":"ref145","first-page":"91","article-title":"Topological methods for the analysis of high dimensional data sets and 3D object recognition","volume":"2","author":"Singh","year":"2007","journal-title":"PBG@Eurographics"},{"key":"ref146","doi-asserted-by":"publisher","DOI":"10.1109\/VAST47406.2019.8986948"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.3390\/app12136799"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.3390\/info11090426"},{"key":"ref149","doi-asserted-by":"publisher","DOI":"10.1109\/MCG.2022.3199727"},{"key":"ref150","article-title":"UMAP: Uniform manifold approximation and projection for dimension reduction","author":"McInnes","year":"2018","journal-title":"arXiv:1802.03426"},{"key":"ref151","article-title":"On the decision boundary of deep neural networks","author":"Li","year":"2018","journal-title":"arXiv:1808.05385"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA51294.2020.00025"},{"key":"ref153","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-022-06149-0"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14650"},{"key":"ref155","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3063289"},{"key":"ref156","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.120"},{"key":"ref157","article-title":"Counterfactual explanations and algorithmic recourses for machine learning: A review","author":"Verma","year":"2020","journal-title":"arXiv:2010.10596"},{"key":"ref158","doi-asserted-by":"publisher","DOI":"10.1145\/3375627.3375850"},{"key":"ref159","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-022-00906-4"},{"key":"ref160","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/876"},{"key":"ref161","article-title":"Model extraction from counterfactual explanations","author":"A\u00efvodji","year":"2020","journal-title":"arXiv:2009.01884"},{"key":"ref162","article-title":"Model reconstruction using counterfactual explanations: Mitigating the decision boundary shift","author":"Dissanayake","year":"2024","journal-title":"arXiv:2405.05369"},{"key":"ref163","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372830"},{"key":"ref164","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86520-7_40"},{"key":"ref165","first-page":"19620","article-title":"Parameterized explainer for graph neural network","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Luo"},{"key":"ref166","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3268797"},{"key":"ref167","first-page":"1","article-title":"GNNexplainer: Generating explanations for graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Ying"},{"key":"ref168","first-page":"12225","article-title":"PGM-Explainer: Probabilistic graphical model explanations for graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Vu"},{"key":"ref169","first-page":"12241","article-title":"On explainability of graph neural networks via subgraph explorations","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yuan"},{"key":"ref170","doi-asserted-by":"publisher","DOI":"10.1515\/9781400881970-018"},{"key":"ref171","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403085"},{"key":"ref172","article-title":"Explainability techniques for graph convolutional networks","author":"Baldassarre","year":"2019","journal-title":"arXiv:1905.13686"},{"key":"ref173","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3204236"},{"key":"ref174","article-title":"GraphFramEx: Towards systematic evaluation of explainability methods for graph neural networks","author":"Amara","year":"2022","journal-title":"arXiv:2206.09677"},{"key":"ref175","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467283"},{"key":"ref176","article-title":"Exploring the limits of language modeling","author":"Jozefowicz","year":"2016","journal-title":"arXiv:1602.02410"},{"key":"ref177","article-title":"Structured attention networks","author":"Kim","year":"2017","journal-title":"arXiv:1702.00887"},{"key":"ref178","first-page":"2397","article-title":"Pythia: A suite for analyzing large language models across training and scaling","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Biderman"},{"key":"ref179","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-3007"},{"key":"ref180","article-title":"Emergent and predictable memorization in large language models","author":"Biderman","year":"2023","journal-title":"arXiv:2304.11158"},{"key":"ref181","first-page":"1","article-title":"BertViz: A tool for visualizing multihead self-attention in the BERT model","volume-title":"Proc. ICLR Workshop: Debugging Mach. Learn. Models","volume":"23","author":"Vig"},{"key":"ref182","article-title":"DeepDecipher: Accessing and investigating neuron activation in large language models","author":"Garde","year":"2023","journal-title":"arXiv:2310.01870"},{"key":"ref183","article-title":"Neuron to graph: Interpreting language model neurons at scale","author":"Foote","year":"2023","journal-title":"arXiv:2305.19911"},{"key":"ref184","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00084"},{"key":"ref185","first-page":"3319","article-title":"Axiomatic attribution for deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"70","author":"Sundararajan"},{"key":"ref186","article-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","author":"Simonyan","year":"2013","journal-title":"arXiv:1312.6034"},{"key":"ref187","article-title":"Visualizing higher-layer features of a deep network","author":"Erhan","year":"1341"},{"key":"ref188","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref189","article-title":"OpenXAI: Towards a transparent evaluation of model explanations","author":"Agarwal","year":"2022","journal-title":"arXiv:2206.11104"},{"key":"ref190","article-title":"The disagreement problem in explainable machine learning: A practitioner\u2019s perspective","author":"Krishna","year":"2022","journal-title":"arXiv:2202.01602"},{"key":"ref191","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2017.10.011"},{"key":"ref192","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0130140"},{"key":"ref193","article-title":"T-explainer: A model-agnostic explainability framework based on gradients","author":"Ortigossa","year":"2024","journal-title":"arXiv:2404.16495"},{"key":"ref194","article-title":"Axiomatic explanations for visual search, retrieval, and similarity learning","author":"Hamilton","year":"2021","journal-title":"arXiv:2103.00370"},{"key":"ref195","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9206975"},{"key":"ref196","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref197","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref198","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00097"},{"key":"ref199","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3089943"},{"key":"ref200","article-title":"Not just a black box: Learning important features through propagating activation differences","author":"Shrikumar","year":"2016","journal-title":"arXiv:1605.01713"},{"key":"ref201","article-title":"SmoothGrad: Removing noise by adding noise","author":"Smilkov","year":"2017","journal-title":"arXiv:1706.03825"},{"key":"ref202","doi-asserted-by":"publisher","DOI":"10.23915\/distill.00022"},{"key":"ref203","first-page":"3145","article-title":"Learning important features through propagating activation differences","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Shrikumar"},{"key":"ref204","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3005510"},{"key":"ref205","first-page":"9269","article-title":"The many Shapley values for model explanation","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Sundararajan"},{"key":"ref206","doi-asserted-by":"publisher","DOI":"10.1038\/s41576-019-0122-6"},{"key":"ref207","article-title":"TreeView: Peeking into deep neural networks via feature-space partitioning","author":"Thiagarajan","year":"2016","journal-title":"arXiv:1611.07429"},{"key":"ref208","article-title":"Model-agnostic interpretability of machine learning","author":"Ribeiro","year":"2016","journal-title":"arXiv:1606.05386"},{"key":"ref209","article-title":"Nothing else matters: Model-agnostic explanations by identifying prediction invariance","author":"Ribeiro","year":"2016","journal-title":"arXiv:1611.05817"},{"key":"ref210","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11491"},{"key":"ref211","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2021.03.012"},{"key":"ref212","article-title":"Investigating decision boundaries of trained neural networks","author":"Yousefzadeh","year":"2019","journal-title":"arXiv:1908.02802"},{"key":"ref213","doi-asserted-by":"publisher","DOI":"10.3390\/make3030027"},{"key":"ref214","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP.2016.7738872"},{"key":"ref215","first-page":"5628","article-title":"Robust and stable black box explanations","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lakkaraju"},{"key":"ref216","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.415"},{"key":"ref217","doi-asserted-by":"publisher","DOI":"10.1006\/jeth.1999.2534"},{"key":"ref218","first-page":"5491","article-title":"Problems with Shapley-value-based explanations as feature importance measures","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Kumar"},{"key":"ref219","doi-asserted-by":"publisher","DOI":"10.1002\/asmb.446"},{"key":"ref220","first-page":"1","article-title":"An efficient explanation of individual classifications using game theory","volume":"11","author":"Strumbelj","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref221","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-013-0679-x"},{"key":"ref222","article-title":"Consistent individualized feature attribution for tree ensembles","author":"Lundberg","year":"2018","journal-title":"arXiv:1802.03888"},{"key":"ref223","doi-asserted-by":"publisher","DOI":"10.1145\/3375627.3375830"},{"key":"ref224","article-title":"A benchmark for interpretability methods in deep neural networks","author":"Hooker","year":"2018","journal-title":"arXiv:1806.10758"},{"key":"ref225","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-021-10057-z"},{"key":"ref226","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-021-10057-z"},{"key":"ref227","article-title":"Feature relevance quantification in explainable AI: A causal problem","author":"Janzing","year":"2019","journal-title":"arXiv:1910.13413"},{"key":"ref228","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376219"},{"key":"ref229","article-title":"Synthetic benchmarks for scientific research in explainable machine learning","author":"Liu","year":"2021","journal-title":"arXiv:2106.12543"},{"key":"ref230","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10050593"},{"key":"ref231","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-023-00933-9"},{"key":"ref232","article-title":"BASED-XAI: Breaking ablation studies down for explainable artificial intelligence","author":"Hameed","year":"2022","journal-title":"arXiv:2207.05566"},{"key":"ref233","first-page":"1","article-title":"The disagreement problem in faithfulness metrics","volume-title":"Proc. NIPS","author":"Barr"},{"key":"ref234","article-title":"On baselines for local feature attributions","author":"Haug","year":"2021","journal-title":"arXiv:2101.00905"},{"key":"ref235","first-page":"5620","article-title":"Variable selection via penalized neural network: A drop-out-one loss approach","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ye"},{"key":"ref236","article-title":"A human-grounded evaluation of SHAP for alert processing","author":"Weerts","year":"2019","journal-title":"arXiv:1907.03324"},{"key":"ref237","first-page":"16749","article-title":"Decisions, counterfactual explanations and strategic behavior","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Tsirtsis"},{"key":"ref238","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-08974-9_56"},{"key":"ref239","article-title":"Towards robust interpretability with self-explaining neural networks","author":"Alvarez-Melis","year":"2018","journal-title":"arXiv:1806.07538"},{"key":"ref240","article-title":"ERASER: A benchmark to evaluate rationalized NLP models","author":"DeYoung","year":"2019","journal-title":"arXiv:1911.03429"},{"key":"ref241","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i17.17745"},{"key":"ref242","article-title":"RISE: Randomized input sampling for explanation of black-box models","author":"Petsiuk","year":"2018","journal-title":"arXiv:1806.07421"},{"key":"ref243","article-title":"Rethinking stability for attribution-based explanations","author":"Agarwal","year":"2022","journal-title":"arXiv:2203.06877"},{"issue":"34","key":"ref244","first-page":"1","article-title":"Quantus: An explainable AI toolkit for responsible evaluation of neural network explanations and beyond","volume":"24","author":"Hedstr\u00f6m","year":"2023","journal-title":"J. Mach. Learn. Res."},{"key":"ref245","first-page":"5256","article-title":"Which explanation should I choose? A function approximation perspective to characterizing post hoc explanations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Han"},{"key":"ref246","doi-asserted-by":"publisher","DOI":"10.1214\/15-AOS1337"},{"key":"ref247","volume-title":"The Kaggle Book: Data Analysis and Machine Learning for Competitive Data Science","author":"Banachewicz","year":"2022"},{"key":"ref248","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009769707641"},{"key":"ref249","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmva.2003.08.006"},{"key":"ref250","doi-asserted-by":"publisher","DOI":"10.1145\/2166966.2166996"},{"key":"ref251","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(21)00208-9"},{"key":"ref252","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-50334-5_4"},{"key":"ref253","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-022-00898-y"},{"key":"ref254","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aace8c"},{"key":"ref255","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-31037-5"},{"key":"ref256","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00372-6"},{"key":"ref257","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(22)00048-6"},{"key":"ref258","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105608"},{"key":"ref259","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103792"},{"key":"ref260","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2993291"},{"key":"ref261","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03922-4"},{"key":"ref262","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3021387"},{"key":"ref263","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-12281-8"},{"key":"ref264","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-2896-2"},{"key":"ref265","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-021-01252-x"},{"key":"ref266","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-022-01980-3"},{"key":"ref267","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-023-01940-w"},{"key":"ref268","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01438"},{"key":"ref269","doi-asserted-by":"publisher","DOI":"10.1016\/j.psychres.2021.114135"},{"key":"ref270","doi-asserted-by":"publisher","DOI":"10.1109\/ICKII50300.2020.9318912"},{"key":"ref271","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2021.108105"},{"key":"ref272","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445083"},{"key":"ref273","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2019.105405"},{"key":"ref274","article-title":"Machine learning for scent: Learning generalizable perceptual representations of small molecules","author":"Sanchez-Lengeling","year":"2019","journal-title":"arXiv:1910.10685"},{"key":"ref275","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6_18"},{"key":"ref276","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1820657116"},{"key":"ref277","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.abe4166"},{"key":"ref278","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.abn9545"},{"key":"ref279","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-020-00479-8"},{"key":"ref280","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-00236-4"},{"key":"ref281","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-04086-x"},{"key":"ref282","doi-asserted-by":"publisher","DOI":"10.21105\/joss.00786"},{"key":"ref283","article-title":"InterpretML: A unified framework for machine learning interpretability","author":"Nori","year":"2019","journal-title":"arXiv:1909.09223"},{"key":"ref284","article-title":"Captum: A unified and generic model interpretability library for PyTorch","author":"Kokhlikyan","year":"2020","journal-title":"arXiv:2009.07896"},{"issue":"93","key":"ref285","first-page":"1","article-title":"iNNvestigate neural networks!","volume":"20","author":"Alber","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref286","article-title":"One explanation does not fit all: A toolkit and taxonomy of AI explainability techniques","author":"Arya","year":"2019","journal-title":"arXiv:1909.03012"},{"key":"ref287","doi-asserted-by":"publisher","DOI":"10.1145\/3430984.3430987"},{"issue":"181","key":"ref288","first-page":"1","article-title":"Alibi explain: Algorithms for explaining machine learning models","volume":"22","author":"Klaise","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref289","article-title":"OmniXAI: A library for explainable AI","author":"Yang","year":"2022","journal-title":"arXiv:2206.01612"},{"key":"ref290","doi-asserted-by":"publisher","DOI":"10.1080\/09540091.2017.1310182"},{"key":"ref291","doi-asserted-by":"publisher","DOI":"10.1038\/540525a"},{"key":"ref292","article-title":"Stakeholders in explainable AI","author":"Preece","year":"2018","journal-title":"arXiv:1810.00184"},{"key":"ref293","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0048-x"},{"key":"ref294","article-title":"Mapping the potential of explainable artificial intelligence (XAI) for fairness along the AI lifecycle","author":"Deck","year":"2024","journal-title":"arXiv:2404.18736"},{"key":"ref295","doi-asserted-by":"publisher","DOI":"10.1093\/0195161211.001.0001"},{"key":"ref296","volume-title":"Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets","volume":"1","author":"Taleb","year":"2005"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10380310\/10549884.pdf?arnumber=10549884","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T20:48:22Z","timestamp":1719348502000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10549884\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":296,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3409843","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}