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Surv."],"published-print":{"date-parts":[[2024,7,31]]},"abstract":"<jats:p>\n                    As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing DNNs become more complex and diverse, ranging from improving a conventional model accuracy metric to infusing advanced human virtues such as fairness, accountability, transparency, and unbiasedness. Recently, techniques in Explainable Artificial Intelligence (XAI) have been attracting considerable attention and have tremendously helped Machine Learning (ML) engineers in understand AI models. However, at the same time, we started to witness the emerging need beyond XAI among AI communities; based on the insights learned from XAI, how can we better empower ML engineers in steering their DNNs so that the model\u2019s reasonableness and performance can be improved as intended? This article provides a timely and extensive literature overview of the field Explanation-Guided Learning (EGL), a domain of techniques that steer the DNNs\u2019 reasoning process by adding regularization, supervision, or intervention on model explanations. In doing so, we first provide a formal definition of EGL and its general learning paradigm. Second, an overview of the key factors for EGL evaluation, as well as summarization and categorization of existing evaluation procedures and metrics for EGL are provided. Finally, the current and potential future application areas and directions of EGL are discussed, and an extensive experimental study is presented aiming at providing comprehensive comparative studies among existing EGL models in various popular application domains, such as Computer Vision and Natural Language Processing domains. Additional resources related to event prediction are included in the article website:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/kugaoyang.github.io\/EGL\/\">https:\/\/kugaoyang.github.io\/EGL\/<\/jats:ext-link>\n                  <\/jats:p>","DOI":"10.1145\/3644073","type":"journal-article","created":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T07:01:30Z","timestamp":1707721290000},"page":"1-39","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":66,"title":["Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning"],"prefix":"10.1145","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8045-2001","authenticated-orcid":false,"given":"Yuyang","family":"Gao","sequence":"first","affiliation":[{"name":"Emory University, Atlanta, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6306-5684","authenticated-orcid":false,"given":"Siyi","family":"Gu","sequence":"additional","affiliation":[{"name":"Emory University, Atlanta, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9675-0903","authenticated-orcid":false,"given":"Junji","family":"Jiang","sequence":"additional","affiliation":[{"name":"Emory University, Atlanta, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6050-5404","authenticated-orcid":false,"given":"Sungsoo Ray","family":"Hong","sequence":"additional","affiliation":[{"name":"George Mason University, Fairfax, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2082-0834","authenticated-orcid":false,"given":"Dazhou","family":"Yu","sequence":"additional","affiliation":[{"name":"Emory University, Atlanta, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2648-9989","authenticated-orcid":false,"given":"Liang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Emory University, Atlanta, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,4,9]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.2174\/156720512801322573"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2870052"},{"key":"e_1_3_2_4_2","first-page":"9525","article-title":"Sanity checks for saliency maps","volume":"31","author":"Adebayo Julius","year":"2018","unstructured":"Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. 2018. 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