scholarly journals Tolerating Ambiguity: Reflections on the Schrems II Ruling

2021 ◽  
Vol 4 (2) ◽  
pp. 75-85
Author(s):  
Susanna Lindroos-Hovinheimo

This paper considers the European Court of Justice’s Schrems II ruling from a variety of angles. From a strictly legal point of view, considering the GDPR, the CJEU came to a logical conclusion. In this paper, I nevertheless try to think about other ways of understanding the dispute and the ruling. In addition to data protection law, the case is about surveillance, platform power, resistance, global politics, data territoriality and the Court’s competence. These sensitive issues come forth when the strict data protection issues are set aside and a slightly more open analysis undertaken. In the end, however, the ruling does bring about real-life problems that pertain to data protection law. Transfers of data to third countries are a pressing problem that no one seems to know how to solve. 

Author(s):  
Dimitar Christozov ◽  
Katia Rasheva-Yordanova

The article shares the authors' experiences in training bachelor-level students to explore Big Data applications in solving nowadays problems. The article discusses curriculum issues and pedagogical techniques connected to developing Big Data competencies. The following objectives are targeted: The importance and impact of making rational, data driven decisions in the Big Data era; Complexity of developing and exploring a Big Data Application in solving real life problems; Learning skills to adopt and explore emerging technologies; and Knowledge and skills to interpret and communicate results of data analysis via combining domain knowledge with system expertise. The curriculum covers: The two general uses of Big Data Analytics Applications, which are well distinguished from the point of view of end-user's objectives (presenting and visualizing data via aggregation and summarization [data warehousing: data cubes, dash boards, etc.] and learning from Data [data mining techniques]); Organization of Data Sources: distinction of Master Data from Operational Data, in particular; Extract-Transform-Load (ETL) process; and Informing vs. Misinforming, including the issue of over-trust vs. under-trust of obtained analytical results.


2016 ◽  
Vol 32 (2) ◽  
Author(s):  
Elena Niculina Dragoi ◽  
Silvia Curteanu

AbstractDifferential evolution (DE), belonging to the evolutionary algorithm class, is a simple and powerful optimizer with great potential for solving different types of synthetic and real-life problems. Optimization is an important aspect in the chemical engineering area, especially when striving to obtain the best results with a minimum of consumed resources and a minimum of additional by-products. From the optimization point of view, DE seems to be an attractive approach for many researchers who are trying to improve existing systems or to design new ones. In this context, here, a review of the most important approaches applying different versions of DE (simple, modified, or hybridized) for solving specific chemical engineering problems is realized. Based on the idea that optimization can be performed at different levels, two distinct cases were considered – process and model optimization. In both cases, there are a multitude of problems solved, from different points of view and with various parameters, this large area of successful applications indicating the flexibility and performance of DE.


Author(s):  
Anita Moum

The objective of this chapter is to identify the role of BIMs in the architectural design process from the practitioners’ point of view. The chapter investigates the main factors affecting the practitioners’ use of BIM, and how BIM impacts their work and interactions. The chapter presents a holistic research approach as well as the findings from its application in four real-life projects. In these projects, much of the practitioners’ focus was on upgrading skills and improving technology. Nevertheless, a number of their challenges were linked to the nature of the architectural design process, particularly to its “hardto- grasp” iterative and intuitive features. A conclusion of this research indicates that the role of BIM is affected by the many interdependencies, relations and interfaces embedded in the highly complex and partly unpredictable real world practice. A future challenge would be to understand, master and balance these relationships - upstream and downstream across multiple levels, processes and activities. The presented holistic research approach and the related findings contributed to research which aimed to embrace the complexity of real-life problems and gain a more comprehensive understanding of what is happening in practice.


2010 ◽  
Vol 25 (3) ◽  
pp. 249-279 ◽  
Author(s):  
Roman Barták ◽  
Miguel A. Salido ◽  
Francesca Rossi

AbstractDuring recent years, the development of new techniques for constraint satisfaction, planning, and scheduling has received increased attention, and substantial effort has been invested in trying to exploit such techniques to find solutions to real-life problems. In this paper, we present a survey on constraint satisfaction, planning, and scheduling from the Artificial Intelligence point of view. In particular, we present the main definitions and techniques, and discuss possible ways of integrating such techniques. We also analyze the role of constraint satisfaction in planning and scheduling, and hint at some open research issues related to planning, scheduling, and constraint satisfaction.


2015 ◽  
Vol 38 (2) ◽  
pp. 371-384 ◽  
Author(s):  
Sukru Acitas ◽  
Birdal Senoglu ◽  
Olcay Arslan

<p>The alpha-skew normal (ASN) distribution has been proposed recently in the literature by using standard normal distribution and a skewing approach. Although ASN distribution is able to model both skew and bimodal data, it is shortcoming when data has thinner or thicker tails than normal. Therefore, we propose an alpha-skew generalized t (ASGT) by using the generalized t (GT) distribution and a new skewing procedure. From this point of view, ASGT can be seen as an alternative skew version of GT distribution. However, ASGT differs from the previous skew versions of GT distribution since it is able to model bimodal data sest as well as it nests most commonly used density functions. In this paper, moments and maximum likelihood estimation of the parameters of ASGT distribution are given. Skewness and kurtosis measures are derived based on the first four noncentral moments. The cumulative distribution function (cdf) of ASGT distribution is also obtained. In the application part of the study, two real life problems taken from the literature are modeled by using ASGT distribution.</p>


Author(s):  
Sandra Wachter ◽  
Brent Mittelstadt

Big Data analytics and artificial intelligence (AI) draw non-intuitive and unverifiable inferences and predictions about the behaviors, preferences, and private lives of individuals. These inferences draw on highly diverse and feature-rich data of unpredictable value, and create new opportunities for discriminatory, biased, and invasive decision-making. Concerns about algorithmic accountability are often actually concerns about the way in which these technologies draw privacy invasive and non-verifiable inferences about us that we cannot predict, understand, or refute.Data protection law is meant to protect people’s privacy, identity, reputation, and autonomy, but is currently failing to protect data subjects from the novel risks of inferential analytics. The broad concept of personal data in Europe could be interpreted to include inferences, predictions, and assumptions that refer to or impact on an individual. If seen as personal data, individuals are granted numerous rights under data protection law. However, the legal status of inferences is heavily disputed in legal scholarship, and marked by inconsistencies and contradictions within and between the views of the Article 29 Working Party and the European Court of Justice.As we show in this paper, individuals are granted little control and oversight over how their personal data is used to draw inferences about them. Compared to other types of personal data, inferences are effectively ‘economy class’ personal data in the General Data Protection Regulation (GDPR). Data subjects’ rights to know about (Art 13-15), rectify (Art 16), delete (Art 17), object to (Art 21), or port (Art 20) personal data are significantly curtailed when it comes to inferences, often requiring a greater balance with controller’s interests (e.g. trade secrets, intellectual property) than would otherwise be the case. Similarly, the GDPR provides insufficient protection against sensitive inferences (Art 9) or remedies to challenge inferences or important decisions based on them (Art 22(3)).This situation is not accidental. In standing jurisprudence the European Court of Justice (ECJ; Bavarian Lager, YS. and M. and S., and Nowak) and the Advocate General (AG; YS. and M. and S. and Nowak) have consistently restricted the remit of data protection law to assessing the legitimacy of input personal data undergoing processing, and to rectify, block, or erase it. Critically, the ECJ has likewise made clear that data protection law is not intended to ensure the accuracy of decisions and decision-making processes involving personal data, or to make these processes fully transparent.Conflict looms on the horizon in Europe that will further weaken the protection afforded to data subjects against inferences. Current policy proposals addressing privacy protection (the ePrivacy Regulation and the EU Digital Content Directive) fail to close the GDPR’s accountability gaps concerning inferences. At the same time, the GDPR and Europe’s new Copyright Directive aim to facilitate data mining, knowledge discovery, and Big Data analytics by limiting data subjects’ rights over personal data. And lastly, the new Trades Secrets Directive provides extensive protection of commercial interests attached to the outputs of these processes (e.g. models, algorithms and inferences).In this paper we argue that a new data protection right, the ‘right to reasonable inferences’, is needed to help close the accountability gap currently posed ‘high risk inferences’ , meaning inferences that are privacy invasive or reputation damaging and have low verifiability in the sense of being predictive or opinion-based. In cases where algorithms draw ‘high risk inferences’ about individuals, this right would require ex-ante justification to be given by the data controller to establish whether an inference is reasonable. This disclosure would address (1) why certain data is a relevant basis to draw inferences; (2) why these inferences are relevant for the chosen processing purpose or type of automated decision; and (3) whether the data and methods used to draw the inferences are accurate and statistically reliable. The ex-ante justification is bolstered by an additional ex-post mechanism enabling unreasonable inferences to be challenged. A right to reasonable inferences must, however, be reconciled with EU jurisprudence and counterbalanced with IP and trade secrets law as well as freedom of expression and Article 16 of the EU Charter of Fundamental Rights: the freedom to conduct a business.


2018 ◽  
Vol 57 (3) ◽  
pp. 437-489 ◽  
Author(s):  
Jonathan McCully

On June 27, 2017, the Grand Chamber of the European Court of Human Rights (GC) delivered its judgment in Satakunnan Markkinapörssi Oy and Satamedia Oy v. Finland. It was the first time that the GC considered whether the application of data protection law to the publishing activities of a media outlet had violated the right to freedom of expression under the European Convention on Human Rights (Convention). In its judgment, the GC found that a prohibition on two companies publishing the taxation data of 1.2 million identifiable individuals had not violated the right to freedom of expression.


2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Shatha Hasan ◽  
Nadir Djeddi ◽  
Mohammed Al-Smadi ◽  
Shrideh Al-Omari ◽  
Shaher Momani ◽  
...  

AbstractThis paper deals with the generalized Bagley–Torvik equation based on the concept of the Caputo–Fabrizio fractional derivative using a modified reproducing kernel Hilbert space treatment. The generalized Bagley–Torvik equation is studied along with initial and boundary conditions to investigate numerical solution in the Caputo–Fabrizio sense. Regarding the generalized Bagley–Torvik equation with initial conditions, in order to have a better approach and lower cost, we reformulate the issue as a system of fractional differential equations while preserving the second type of these equations. Reproducing kernel functions are established to construct an orthogonal system used to formulate the analytical and approximate solutions of both equations in the appropriate Hilbert spaces. The feasibility of the proposed method and the effect of the novel derivative with the nonsingular kernel were verified by listing and treating several numerical examples with the required accuracy and speed. From a numerical point of view, the results obtained indicate the accuracy, efficiency, and reliability of the proposed method in solving various real life problems.


Author(s):  
I. M. RASSOLOV ◽  
S. G. CHUBUKOVA

The principles of emerging legislation devoted to the processing of genetic information take their place among the informal legal phenomena. The identification of these principles is the task of the legal science and, in particular, of data protection law. From the point of view of data protection law, the article presents a new author’s approach to the construction of the system of principles of the legal regulation of genetic information. These principles include: the principle of responsibility to future generations; the principle of freedom of scientific research; the principle of protection of human dignity; the principle of privacy. Genome protection is aimed not only at preserving the life and health of a particular person, but also at preserving the genome of his or her descendants. This makes it possible to consider the genome as a heritage of mankind. Freedom of scientific research in the field of genetics implies the freedom to study genetic information, but not the freedom to use it. With regard to scientific research of genetic information of representatives of a particular population, in addition to individual consent to the processing of such information, allowance is made for the consent expressed through the legitimate representatives of the groups or peoples concerned. The ideas of extended and open consent of the person to the processing of genetic information are analyzed. The conclusion is made about the necessity of fixing the system of principles of legal regulation in the field of genetic information processing in a special law «On genetic information.»


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