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Increasingly, these models are utilizing algorithmic learning or \u201cartificial intelligence\u201d (AI). The rapid growth of AI in the liquefaction literature is unsurprising, given its ease of implementation and potential advantages over traditional statistical methods. However, AI liquefaction models have been widely ignored by practitioners and researchers alike; the objective of this article is to investigate \u201cwhy?\u201d Through a sample review of 75 publications, we identify several good reasons. Namely, these models frequently (1) are not compared to state\u2010of\u2010practice models, making it unclear why they should be adopted; (2) depart from best practices in model development; (3) use AI in ways that may not be useful; (4) are presented in ways that overstate their complexity and make them unapproachable; and (5) are discussed but not actually provided, meaning that no one can use the models even if they wanted to. These prevailing problems must be understood, identified, and remedied, but this does not mean that AI itself is problematic or that all prior efforts have been without merit or utility. Instead, understanding these recurrent shortcomings can help improve the direction and perceptions of this growing body of work. Toward this end, we highlight papers that are generally free from these shortcomings, and which demonstrate applications where AI is more likely to provide value in the near\u2010term: permitting new modeling approaches and potentially improving predictions of liquefaction phenomena.<\/jats:p>","DOI":"10.1177\/87552930231173711","type":"journal-article","created":{"date-parts":[[2023,5,24]],"date-time":"2023-05-24T08:25:03Z","timestamp":1684916703000},"page":"1883-1910","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":19,"title":["Why \u201cAI\u201d models for predicting soil liquefaction have been ignored, plus some that shouldn\u2019t be"],"prefix":"10.1002","volume":"39","author":[{"given":"Brett W","family":"Maurer","sequence":"first","affiliation":[{"name":"Department of Civil &amp; Environmental Engineering University of Washington Seattle WA USA"}]},{"given":"Morgan D","family":"Sanger","sequence":"additional","affiliation":[{"name":"Department of Civil &amp; Environmental Engineering University of Washington Seattle WA USA"}]}],"member":"311","published-online":{"date-parts":[[2023,8]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10706-016-0004-z"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1177\/87552930211032685"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-48044-0"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1973\/1\/012197"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)1090-0241(2000)126:11(1015)"},{"key":"e_1_2_9_7_1","unstructured":"AsadiA BaiseL ChatterjeeS ChanskyA MoaveniB(2022)Global geospatial liquefaction model updates using advanced machine learning algorithms. 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