{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T12:10:27Z","timestamp":1773490227606,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":12,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,3,23]]},"DOI":"10.1145\/3742414.3794713","type":"proceedings-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T11:03:52Z","timestamp":1773054232000},"page":"118-120","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["XAI Effect on Laypeople VS Experts Perceptions of AI Outcomes - Preliminary Work"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2091-2966","authenticated-orcid":false,"given":"Avital","family":"Shulner-Tal","sequence":"first","affiliation":[{"name":"Department of Software Engineering & Information Systems, Braude College of Engineering, Karmiel, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9092-7231","authenticated-orcid":false,"given":"Julia","family":"Sheidin","sequence":"additional","affiliation":[{"name":"Department of Software Engineering & Information Systems, Braude College of Engineering, Karmiel, Israel"}]}],"member":"320","published-online":{"date-parts":[[2026,3,22]]},"reference":[{"key":"e_1_3_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-90403-0_2"},{"key":"e_1_3_3_3_3_2","doi-asserted-by":"crossref","unstructured":"Sajid Ali Tamer Abuhmed Shaker El-Sappagh Khan Muhammad Jose\u00a0M Alonso-Moral Roberto Confalonieri Riccardo Guidotti Javier Del\u00a0Ser Natalia D\u00edaz-Rodr\u00edguez and Francisco Herrera. 2023. Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence. Information fusion 99 (2023) 101805.","DOI":"10.1016\/j.inffus.2023.101805"},{"key":"e_1_3_3_3_4_2","doi-asserted-by":"crossref","unstructured":"Plamen\u00a0P Angelov Eduardo\u00a0A Soares Richard Jiang Nicholas\u00a0I Arnold and Peter\u00a0M Atkinson. 2021. Explainable artificial intelligence: an analytical review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 11 5 (2021) e1424.","DOI":"10.1002\/widm.1424"},{"key":"e_1_3_3_3_5_2","doi-asserted-by":"crossref","unstructured":"Alejandro\u00a0Barredo Arrieta Natalia D\u00edaz-Rodr\u00edguez Javier Del\u00a0Ser Adrien Bennetot Siham Tabik Alberto Barbado Salvador Garc\u00eda Sergio Gil-L\u00f3pez Daniel Molina Richard Benjamins et\u00a0al. 2020. Explainable Artificial Intelligence (XAI): Concepts taxonomies opportunities and challenges toward responsible AI. Information fusion 58 (2020) 82\u2013115.","DOI":"10.1016\/j.inffus.2019.12.012"},{"key":"e_1_3_3_3_6_2","unstructured":"Vijay Arya Rachel\u00a0KE Bellamy Pin-Yu Chen Amit Dhurandhar Michael Hind Samuel\u00a0C Hoffman Stephanie Houde Q\u00a0Vera Liao Ronny Luss Aleksandra Mojsilovi\u0107 et\u00a0al. 2019. One explanation does not fit all: A toolkit and taxonomy of ai explainability techniques. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1909.03012 (2019)."},{"key":"e_1_3_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3172944.3172961"},{"key":"e_1_3_3_3_8_2","doi-asserted-by":"crossref","unstructured":"Riccardo Guidotti Anna Monreale Salvatore Ruggieri Franco Turini Fosca Giannotti and Dino Pedreschi. 2018. A survey of methods for explaining black box models. ACM computing surveys (CSUR) 51 5 (2018) 1\u201342.","DOI":"10.1145\/3236009"},{"key":"e_1_3_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3450614.3463354"},{"key":"e_1_3_3_3_10_2","doi-asserted-by":"crossref","unstructured":"Angel Pina Corbin Petersheim Josh Cherian Joanna\u00a0Nicole Lahey Gerianne Alexander and Tracy Hammond. 2023. Using machine learning with eye-tracking data to predict if a recruiter will approve a resume. Machine Learning and Knowledge Extraction 5 3 (2023) 713\u2013724.","DOI":"10.3390\/make5030038"},{"key":"e_1_3_3_3_11_2","doi-asserted-by":"crossref","unstructured":"Donghee Shin. 2021. The effects of explainability and causability on perception trust and acceptance: Implications for explainable AI. International journal of human-computer studies 146 (2021) 102551.","DOI":"10.1016\/j.ijhcs.2020.102551"},{"key":"e_1_3_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3708319.3733707"},{"key":"e_1_3_3_3_13_2","doi-asserted-by":"crossref","unstructured":"Selim S\u00fcr\u00fcc\u00fc Berk K\u00fc\u00e7\u00fck and Mustafa\u00a0Kemal Ayd\u0131n. 2024. An example of the application of artificial intelligence models in human resources processes. Afyon Kocatepe \u00dcniversitesi \u0130ktisadi ve \u0130dari Bilimler Fak\u00fcltesi Dergisi 26 \u00d6zel Say\u0131 (2024) 101\u2013116.","DOI":"10.33707\/akuiibfd.1443940"}],"event":{"name":"IUI '26: 31st International Conference on Intelligent User Interfaces","location":"Paphos Cyprus","acronym":"IUI '26 Companion","sponsor":["SIGCHI ACM Special Interest Group on Computer-Human Interaction","SIGAI ACM Special Interest Group on Artificial Intelligence"]},"container-title":["Companion Proceedings of the 31st International Conference on Intelligent User Interfaces"],"original-title":[],"deposited":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T11:05:23Z","timestamp":1773486323000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3742414.3794713"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,22]]},"references-count":12,"alternative-id":["10.1145\/3742414.3794713","10.1145\/3742414"],"URL":"https:\/\/doi.org\/10.1145\/3742414.3794713","relation":{},"subject":[],"published":{"date-parts":[[2026,3,22]]},"assertion":[{"value":"2026-03-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}