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However, these techniques are often so complex that they fail to provide simple and understandable explanations for the outputs they generate. To address this issue, the field of\n            <jats:italic>explainable artificial intelligence<\/jats:italic>\n            has recently emerged. However, most data generated in different domains are inherently structural; that is, they consist of parts and relationships among them. Such data can be represented using either a simple\n            <jats:italic>data-structure<\/jats:italic>\n            or\n            <jats:italic>form<\/jats:italic>\n            , such as a\n            <jats:italic>vector<\/jats:italic>\n            , or a complex data-structure, such as a\n            <jats:italic>graph<\/jats:italic>\n            . The effect of this representation form on the explainability and interpretability of machine learning models is not extensively discussed in the literature. In this survey article, we review efficient algorithms proposed for learning from inherently structured data, emphasizing how their representation form affects the explainability of learning models. A conclusion of our literature review is that using complex\n            <jats:italic>forms<\/jats:italic>\n            or\n            <jats:italic>data-structures<\/jats:italic>\n            for data representation improves not only the learning performance but also the explainability and transparency of the model.\n          <\/jats:p>","DOI":"10.1145\/3662178","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T11:24:06Z","timestamp":1714389846000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A Review on the Impact of Data Representation on Model Explainability"],"prefix":"10.1145","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3436-0541","authenticated-orcid":false,"given":"Mostafa","family":"Haghir Chehreghani","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran (the Islamic Republic of)"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,6,22]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2011.12.002"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i14.17482"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/116873.116880"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3050563"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1110\/ps.062189906"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.12.012"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3341161.3342958"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570457"},{"key":"e_1_3_2_10_2","first-page":"2092","volume-title":"Encyclopedia of Statistical Sciences","author":"Bowman Kimiko O.","year":"1998","unstructured":"Kimiko O. 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