The right to be forgotten in data protection law: a search for the concept of protection

2015 ◽  
Vol 5 (3) ◽  
pp. 249
Author(s):  
Maximilian Von Grafenstein ◽  
Wolfgang Schulz
Author(s):  
Helena U. Vrabec

Chapter 7 analyses the right to data portability set out in Article 20 of the GDPR. It first provides an overview of several commercial and regulatory initiatives that preceded the GDPR version of the right to personal data portability. Next, it explores the language of Article 20 to demonstrate the effects of the narrow scope of the right. The chapter then shows how data portability interacts with other data subject rights, particularly with the right to access and the right to be forgotten, before it describes manifestations of data portability in legal areas outside of the data protection law. Finally, the chapter explores the specific objective of the right to data portability under the GDPR as an enabler of data subjects’ control.


Author(s):  
Helena U. Vrabec

Chapter 5 focuses on Article 15 of the GDPR and explains the scope of the information that can be accessed under the right. The chapter then discusses the importance of the interface to submit data subject access requests. The core part of Chapter 5 is the analysis of the regulatory boundaries of the right of access and various avenues to limit the right, for instance, a conflict with the rights of another individual. Finally, the chapter illustrates how the right of access is applied in the data-driven economy by applying it to three different contexts: shared data, anonymised/pseudonymised data, and automated decision-making.


2020 ◽  
Vol 21 (S1) ◽  
pp. 55-65
Author(s):  
Federico Fabbrini ◽  
Edoardo Celeste

AbstractThis article explores the challenges of the extraterritorial application of the right to be forgotten and, more broadly, of EU data protection law in light of the recent case law of the ECJ. The paper explains that there are good arguments for the EU to apply its high data protection standards outside its borders, but that such an extraterritorial application faces challenges, as it may clash with duties of international comity, legal diversity, or contrasting rulings delivered by courts in other jurisdictions. As the article points out from a comparative perspective, the protection of privacy in the digital age increasingly exposes a tension between efforts by legal systems to impose their high standards of data protection outside their borders – a dynamic which could be regarded as ‘imperialist’ – and claims by other legal systems to assert their own power over data – a dynamic which one could name ‘sovereigntist’. As the article suggests, navigating between the Scylla of imperialism and the Charybdis of sovereigntism will not be an easy task. In this context, greater convergence in the data protection framework of liberal democratic systems worldwide appears as the preferable path to secure privacy in the digital age.


2020 ◽  
Author(s):  
Frederik Zuiderveen Borgesius

Algorithmic decision-making and other types of artificial intelligence (AI) can be used to predict who will commit crime, who will be a good employee, who will default on a loan, etc. However, algorithmic decision-making can also threaten human rights, such as the right to non-discrimination. The paper evaluates current legal protection in Europe against discriminatory algorithmic decisions. The paper shows that non-discrimination law, in particular through the concept of indirect discrimination, prohibits many types of algorithmic discrimination. Data protection law could also help to defend people against discrimination. Proper enforcement of non-discrimination law and data protection law could help to protect people. However, the paper shows that both legal instruments have severe weaknesses when applied to artificial intelligence. The paper suggests how enforcement of current rules can be improved. The paper also explores whether additional rules are needed. The paper argues for sector-specific – rather than general – rules, and outlines an approach to regulate algorithmic decision-making.


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