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"Legal dilemmas of Estonian artificial intelligence strategy: in between of e-society and global race"

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https://link.springer.com/epdf/10.1007/s00146-020-01009-8?
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... Through national laws on AI and data protection, the recommendation still needs to be enacted and put into effect. Less than two-thirds of countries in the globe have privacy laws that guarantee that personal data of residents being subject to strict protections and restrictions (Kerikmäe & Pärn-Lee, 2021;Dwivedi et al., 2021). Beyond such general guidelines, policymakers in the education sector need to gain a deeper understanding of the particular ethical problems that arise from the use of AI in education, such as its reliance on specific pedagogies, and offer actionable advice on how to control the use of student data, prevent algorithm discrimination, limit the use of intrusive AI tools, and support human agency in AI supported learning environments (Miao & Holmes, 2022;Park, Ahn, Hosanagar, & Lee, 2022). ...
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... This sincerity question is a well-known yet severe problem in general; it is notoriously difficult to audit proprietary automatic decision-making systems [69]. And once again, the problem is not merely about auditing; it is about the use of private sector systems for public sector services [35,38,63]. In fact, the system W uses also a third proprietary system, W * * , which translates the documents. ...
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The General Data Protection Regulation (GDPR) came into force in 2018. After this enforcement, many fines have already been imposed by national data protection authorities in Europe. This paper examines the individual GDPR articles referenced in the enforcement decisions, as well as predicts the amount of enforcement fines with available meta-data and text mining features extracted from the enforcement decision documents. According to the results, articles related to the general principles, lawfulness, and information security have been the most frequently referenced ones. Although the amount of fines imposed vary across the articles referenced, these three particular articles do not stand out. Furthermore, a better statistical evidence is available with other meta-data features, including information about the particular European countries in which the enforcements were made. Accurate predictions are attainable even with simple machine learning techniques for regression analysis. Basic text mining features outperform the meta-data features in this regard. In addition to these results, the paper reflects the GDPR's enforcement against public administration obstacles in the European Union (EU), as well as discusses the use of automatic decision-making systems in judiciary.
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