Browsing by Subject "Ownership"

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Open Access
Legal aspects of artificial intelligence in health: an overview under Turkish and comparative law
(CRC Press, 2024-01-01) Özçelik, Şemsi Barış; Köse, Utku; Demirezen, Mustafa Umut
Healthcare is one of the areas where artificial intelligence (AI) is most widely used. Legal issues surrounding AI in health can be gathered in two main themes, namely, fundamental rights (data protection, privacy law, and the prohibition of discrimination) and liability law. Against the risks arising from fundamental rights violations, healthcare service providers should deploy reliable AI systems, determine the purposes of data collection and processing in as much detail as possible and inform the data owners, monitor whether the data is used for purposes other than the foreseen, determine the role and extent of human intervention in the use of AI systems, and train employees with up-to-date information on fundamental rights. Where possible and suitable for the purpose, synthetic, i.e. computer-generated data should also be used. As regards liability law, there is a need for regulation, at least in certain matters. Product safety rules, standardization principles, and technologies that are not allowed, if any, should be determined. Regulations that include software within the scope of product liability and, when necessary, provide convenience to possible victims in terms of fault and causal link should be preferred. In the long term, technological development should be monitored and solutions such as compensation funds should be considered, if necessary. All these suggested regulations will positively impact the development of technology, as they will provide legal certainty not only for consumers but also for those who develop and use AI technologies in the health sector.
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Unknown unknowns: knightian uncertainty and corporate opportunistic earnings management
(Wiley, 2024-01-01) Yao, Shouyu; Xie, Xiaochen; Boubaker, Sabri; Şensoy, Ahmet; Cheng, Feiyang
Uncertainty is inherent in the real world. Faced with Knightian uncertainty caused by many extreme events, this paper focuses on the analysis of corporate opportunistic earnings management behaviour under the unknown unknowns framework. This paper finds that with an increase in market Knightian uncertainty, corporations will significantly adopt both accrual earnings management and real earnings management. More importantly, when compared with upward earnings management, the results indicate that Knightian uncertainty will lead corporations to implement more downward earnings management. Our results are consistent with the big bath theory, which is also verified through the adjustment of non-recurring profit and loss accounts. To understand the real process of earnings management, we also discuss the strategic choice behaviour of earnings management under different heterogeneous situations.