{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T22:40:15Z","timestamp":1768344015869,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":5,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,6,16]]},"DOI":"10.1145\/3769126.3769240","type":"proceedings-article","created":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T14:50:37Z","timestamp":1768315837000},"page":"493-494","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["explainy: A Toolkit for Legal-XAI"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1794-0763","authenticated-orcid":false,"given":"Aniket","family":"Kesari","sequence":"first","affiliation":[{"name":"Fordham University, New York City, New York, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6259-2238","authenticated-orcid":false,"given":"Stefan","family":"Bechtold","sequence":"additional","affiliation":[{"name":"ETH Zurich, Zurich, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6817-7529","authenticated-orcid":false,"given":"Elliott","family":"Ash","sequence":"additional","affiliation":[{"name":"ETH Zurich, Zurich, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0204-8517","authenticated-orcid":false,"given":"Mauro","family":"Luzzatto","sequence":"additional","affiliation":[{"name":"ETH Zurich, Zurich, Switzerland"}]}],"member":"320","published-online":{"date-parts":[[2026,1,13]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"publisher","unstructured":"Aniket Kesari Daniela Sele Elliott Ash and Stefan Bechtold. 2025. Explaining eXplainable AI. SSRN Electronic Journal (May 2025). 10.2139\/ssrn.4972085","DOI":"10.2139\/ssrn.4972085"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"publisher","unstructured":"Mauro Luzzatto Aniket Kesari Stefan Bechtold and Elliott Ash. 2025. explainy: A Toolkit for Legal-XAI. https:\/\/github.com\/Akesari12\/explainy. 10.5281\/zenodo.15353234","DOI":"10.5281\/zenodo.15353234"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"Cynthia Rudin. 2019. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. http:\/\/arxiv.org\/abs\/1811.10154","DOI":"10.1038\/s42256-019-0048-x"},{"key":"e_1_3_3_1_5_2","unstructured":"Andrew\u00a0D. Selbst and Solon Barocas. 2018. The Intuitive Appeal of Explainable Machines. Fordham Law Review 87 3 (2018) 1085\u20131139. https:\/\/ir.lawnet.fordham.edu\/flr\/vol87\/iss3\/11"},{"key":"e_1_3_3_1_6_2","unstructured":"Sandra Wachter Brent D.\u00a0M. Mittelstadt and Chris Russell. 2018. Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR. Harvard Journal of Law and Technology 31 2 (2018) 841\u2013887. https:\/\/arxiv.org\/abs\/1711.00399"}],"event":{"name":"ICAIL 2025: 20th International Conference on Artificial Intelligence and Law","location":"Chicago , IL , USA","acronym":"ICAIL 2025"},"container-title":["Proceedings of the Twentieth International Conference on Artificial Intelligence and Law"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3769126.3769240","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T15:50:36Z","timestamp":1768319436000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3769126.3769240"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,16]]},"references-count":5,"alternative-id":["10.1145\/3769126.3769240","10.1145\/3769126"],"URL":"https:\/\/doi.org\/10.1145\/3769126.3769240","relation":{},"subject":[],"published":{"date-parts":[[2025,6,16]]},"assertion":[{"value":"2026-01-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}