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Whilst these companies are legally obliged to treat users\u2019 information \u201cfairly and responsibly,\u201d artificial intelligence techniques used to process data have become incredibly sophisticated, leading to users\u2019 concerns that a lack of clarity is making it hard to understand the nature and scope of data collection and use.<\/jats:p>\n          <jats:p>There has been little work undertaken on a self-contained user awareness tool targeting VAs. PrivExtractor, a novel web-based awareness dashboard for VA users, intends to redress this imbalance of understanding between the data \u201cprocessors\u201d and the user. It aims to achieve this using the four largest VA vendors as a case study and providing a comparison function that examines the four companies\u2019 privacy practices and their compliance with data protection law.<\/jats:p>\n          <jats:p>As a result of this research, we conclude that the companies studied are largely compliant with the law, as expected. However, the user remains disadvantaged due to the ineffectiveness of current data regulation that does not oblige the companies to fully and transparently disclose how and when they use, share, or profit from the data. Furthermore, the software tool developed during the research is, we believe, the first that is capable of a comparative analysis of VA privacy with a visual demonstration to increase ease of understanding for the user.<\/jats:p>","DOI":"10.1145\/3588770","type":"journal-article","created":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T12:21:36Z","timestamp":1679574096000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["PrivExtractor: Toward Redressing the Imbalance of Understanding between Virtual Assistant Users and Vendors"],"prefix":"10.1145","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1599-8400","authenticated-orcid":false,"given":"Tom","family":"Bolton","sequence":"first","affiliation":[{"name":"School of Science, Engineering and Environment, University of Salford, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0908-6483","authenticated-orcid":false,"given":"Tooska","family":"Dargahi","sequence":"additional","affiliation":[{"name":"Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0069-8552","authenticated-orcid":false,"given":"Sana","family":"Belguith","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Bristol, Bristol, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4715-212X","authenticated-orcid":false,"given":"Carsten","family":"Maple","sequence":"additional","affiliation":[{"name":"Warwick Manufacturing Group (WMG), University of Warwick, Coventry, UK, Maple"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,5,13]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Statista. 2022. 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