{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T08:50:16Z","timestamp":1777107016809,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":14,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,14]]},"DOI":"10.1145\/3787279.3787290","type":"proceedings-article","created":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T07:38:47Z","timestamp":1777102727000},"page":"61-65","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Performance Trade-offs in Explainability-as-a-Service Using Surrogate Linear Models"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9765-1066","authenticated-orcid":false,"given":"Giuseppe","family":"D'Acquisto","sequence":"first","affiliation":[{"name":"LUISS, LUISS, Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5442-2239","authenticated-orcid":false,"given":"Paolo","family":"Fantozzi","sequence":"additional","affiliation":[{"name":"GEPLI, LUMSA, Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0903-398X","authenticated-orcid":false,"given":"Maurizio","family":"Naldi","sequence":"additional","affiliation":[{"name":"GEPLI, LUMSA, Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,25]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Amina Adadi and Mohammed Berrada. 2018. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access 6 (2018) 52138\u201352160.","DOI":"10.1109\/ACCESS.2018.2870052"},{"key":"e_1_3_3_1_3_2","unstructured":"D Anguita A Ghio L Oneto X Parra and Jorge\u00a0Luis Reyes-Ortiz. 2013. A public domain dataset for human Activity Recognition using smartphones. Eur Symp Artif Neural Netw (2013) 437\u2013442."},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"Carlo Combi Beatrice Amico Riccardo Bellazzi Andreas Holzinger Jason\u00a0H Moore Marinka Zitnik and John\u00a0H Holmes. 2022. A manifesto on explainability for artificial intelligence in medicine. Artificial Intelligence in Medicine 133 (2022) 102423.","DOI":"10.1016\/j.artmed.2022.102423"},{"key":"e_1_3_3_1_5_2","unstructured":"Arun Das and Paul Rad. 2020. Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey. (June 2020). arxiv:https:\/\/arXiv.org\/abs\/2006.11371\u00a0[cs.CV]"},{"key":"e_1_3_3_1_6_2","unstructured":"European Parliament. 4 May 2016. Regulation (EU) 2016\/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data and repealing Directive 95\/46\/EC (General Data Protection Regulation) (Text with EEA relevance). Official Journal L 119 (4 May 2016) 1\u201388."},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-81226-2_21"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Paolo Fantozzi and Maurizio Naldi. 2024. The Explainability of Transformers: Current Status and Directions. Computers 13 4 (April 2024) 92.","DOI":"10.3390\/computers13040092"},{"key":"e_1_3_3_1_9_2","unstructured":"Umer Naeem. 2024. Machine Failure Prediction using Sensor data. https:\/\/www.kaggle.com\/datasets\/umerrtx\/machine-failure-prediction-using-sensor-data"},{"key":"e_1_3_3_1_10_2","volume-title":"Machine learning for civil and environmental engineers: A practical approach to data-driven analysis, explainability, and causality","author":"Naser MZ","year":"2023","unstructured":"MZ Naser. 2023. Machine learning for civil and environmental engineers: A practical approach to data-driven analysis, explainability, and causality. John Wiley & Sons."},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2017.58"},{"key":"e_1_3_3_1_13_2","volume-title":"Electronic imaging","author":"Street William\u00a0Nick","year":"1993","unstructured":"William\u00a0Nick Street, William\u00a0H. Wolberg, and Olvi\u00a0L. Mangasarian. 1993. Nuclear feature extraction for breast tumor diagnosis. In Electronic imaging. https:\/\/api.semanticscholar.org\/CorpusID:14922543"},{"key":"e_1_3_3_1_14_2","unstructured":"William Wolberg Olvi Mangasarian and William\u00a0Nick Street. 1995. Breast Cancer Wisconsin (Diagnostic). UCI Machine Learning Repository. DOI: https:\/\/doi.org\/10.24432\/C5DW2B."},{"key":"e_1_3_3_1_15_2","unstructured":"Bing Xu. 2015. Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1505.00853 (2015)."}],"event":{"name":"ICAAI 2025: 2025 9th International Conference on Advances in Artificial Intelligence","location":"Manchester United Kingdom","acronym":"ICAAI 2025"},"container-title":["Proceedings of the 2025 9th International Conference on Advances in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3787279.3787290","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T08:22:15Z","timestamp":1777105335000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3787279.3787290"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,14]]},"references-count":14,"alternative-id":["10.1145\/3787279.3787290","10.1145\/3787279"],"URL":"https:\/\/doi.org\/10.1145\/3787279.3787290","relation":{},"subject":[],"published":{"date-parts":[[2025,11,14]]},"assertion":[{"value":"2026-04-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}