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However, where data are processed by artificial intelligence (AI) algorithms, asserting control and providing adequate explanations is a challenge. Due to massive increases in computing power and big data processing, modern AI algorithms are too complex and opaque to be understood by most data subjects. Articles 15 and 22 of the GDPR provide a modest regulatory framework for automated data processing by, among other things, mandating that data controllers inform data subjects about when it is being used, and its logic and ramifications. Nevertheless, due to the phrasing of the articles and the numerous exceptions they allow, doubts have arisen about their effectiveness. In this paper, we empirically evaluate the quality and effectiveness of AI disclosures as mandated by the GDPR. By means of an online survey (<jats:italic>N<\/jats:italic>\u2009=\u2009835), we investigated how data subjects expect to be informed about the automated processing of their data. We then conducted a content analysis of the AI disclosures of <jats:italic>N<\/jats:italic>\u2009=\u2009100 companies and organizations. The combined findings reveal that current GDPR-mandated disclosures do not meet the expectations and needs of data subjects. Explanations drawn up following the guidelines of the generic formulations of the GDPR differ widely and are often vague, incomplete and lack transparency. In our conclusions we identify a path towards standardizing and optimizing AI information notices.<\/jats:p>","DOI":"10.1007\/s00146-022-01424-z","type":"journal-article","created":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T18:04:15Z","timestamp":1652292255000},"page":"235-256","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["\u201cPlease understand we cannot provide further information\u201d: evaluating content and transparency of GDPR-mandated AI disclosures"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6477-2761","authenticated-orcid":false,"given":"Alexander J.","family":"Wulf","sequence":"first","affiliation":[]},{"given":"Ognyan","family":"Seizov","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,11]]},"reference":[{"key":"1424_CR1","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi A, Berrada M (2018) Peeking inside the black-box: a survey on Explainable Artificial Intelligence (XAI). 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