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In this article, we seek to review and categorize research on\n counterfactual explanations<\/jats:italic>\n , a specific class of explanation that provides a link between what could have happened had input to a model been changed in a particular way. Modern approaches to counterfactual explainability in machine learning draw connections to the established legal doctrine in many countries, making them appealing to fielded systems in high-impact areas such as finance and healthcare. Thus, we design a rubric with desirable properties of counterfactual explanation algorithms and comprehensively evaluate all currently proposed algorithms against that rubric. Our rubric provides easy comparison and comprehension of the advantages and disadvantages of different approaches and serves as an introduction to major research themes in this field. We also identify gaps and discuss promising research directions in the space of counterfactual explainability.\n <\/jats:p>","DOI":"10.1145\/3677119","type":"journal-article","created":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T11:17:01Z","timestamp":1720523821000},"page":"1-42","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review"],"prefix":"10.1145","volume":"56","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-7797-926X","authenticated-orcid":false,"given":"Sahil","family":"Verma","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, University of Washington, Seattle, United States"}]},{"ORCID":"http:\/\/orcid.org\/0009-0005-3885-0195","authenticated-orcid":false,"given":"Varich","family":"Boonsanong","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, University of Washington, Seattle, United States"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8918-7566","authenticated-orcid":false,"given":"Minh","family":"Hoang","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, University of Washington, Seattle, United States"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8721-499X","authenticated-orcid":false,"given":"Keegan","family":"Hines","sequence":"additional","affiliation":[{"name":"Arthur AI, Washington DC, United States"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2231-680X","authenticated-orcid":false,"given":"John","family":"Dickerson","sequence":"additional","affiliation":[{"name":"Arthur AI, Washington DC, United States"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-3797-4293","authenticated-orcid":false,"given":"Chirag","family":"Shah","sequence":"additional","affiliation":[{"name":"University of Washington, Seattle, United States"}]}],"member":"320","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"e_1_3_4_2_2","first-page":"66","volume-title":"Proceedings of the 39th International Conference on Machine Learning","author":"Abid Abubakar","year":"2022","unstructured":"Abubakar Abid, Mert Yuksekgonul, and James Zou. 2022. 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