The Download of the Week is “Report on Civil Liability for Misuse of Private Information” (Simon Constantine (ed), Singapore: Law Reform Committee, Singapore Academy of Law, 2020) on SSRN by Jack Tsen-Ta Lee and Phang Hsiao Chung. Here is the abstract:
This report issued by the Law Reform Committee of the Singapore Academy of Law considers whether the existing legal protections from the disclosure and serious misuse of private information in Singapore are sufficient and effective.
At present, while various protections for victims of such misuse and related breaches of privacy exist, these derive from an assortment of different statutory and common law causes of action (for example, suing for intentional infliction of emotional distress, private nuisance and/or breach of confidence, or bringing claims under the Personal Data Protection Act or Protection from Harassment Act). This patchwork of laws – several of which were designed primarily to address matters other than misuse of private information – not only risks making the law more difficult for victims to navigate, it also risks some instances of serious misuse of private information not being effectively provided for and those affected finding themselves with no real recourse or remedy.
Given these shortcomings, it is submitted that a statutory tort of misuse of private information should be introduced.
(The draft bill annexed to the report was prepared by Phang Hsiao Chung, Deputy Registrar of the Supreme Court, in his capacity as a member of the Law Reform Committee.)
Alan M. Sears (Center for Law and Digital Technologies (eLaw), Leiden Law School, Leiden University) has posted “Algorithmic Speech and Freedom of Expression” (Vanderbilt Journal of Transnational Law, Vol. 53, No. 4, 2020) on SSRN. Here is the abstract:
Algorithms have become increasingly common, and with this development, so have algorithms that approximate human speech. This has introduced new issues with which courts and legislators will have to grapple. Courts in the United States have found that search engine results are a form of speech that is protected by the Constitution, and cases in Europe concerning liability for autocomplete suggestions have led to varied results. Beyond these instances, insight into how courts handle algorithmic speech are few and far between.
By focusing on three categories of algorithmic speech, defined as curated production, interactive/responsive production, and semi-autonomous production, this Article analyzes these various forms of algorithmic speech within the international framework for freedom of expression. After a brief introduction of that framework and a look towards approaches to algorithmic speech in the United States, the Article then examines whether the creators or controllers of different forms of algorithms should be considered content providers or mere intermediaries, the determination of which ultimately has implications for liability, which is also explored. The Article then looks at possible interferences with algorithmic speech, and how such interferences may be examined under the three-part test-particular attention is paid to the balancing of rights and interests at play-in order to answer the question of the extent to which algorithmic speech is worthy of protection under international standards of freedom of expression. Finally, other relevant issues surrounding algorithmic speech are discussed that will have an impact going forward, many of which involve questions of policy and societal values that accompany granting algorithmic speech protection.
Jonatas S. De Souza (Paulista University), Jair M. Abe (Paulista University), Luiz A. De Lima (Paulista University, and Nilson A. De Souza have posted “The Brazilian Law of Personal Data Protection” (International Journal of Network Security & Its Applications (IJNSA) Vol.12, No.6, November 2020) on SSRN. Here is the abstract:
Rapid technological change and globalization have created new challenges when it comes to the protection and processing of personal data. In 2018, Brazil presented a new law that has the proposal to inform how personal data should be collected and treated, to guarantee the security and integrity of the data holder. The General Law Data Protection – LGPD, was sanctioned on September 18th, 2020. Now, the citizen is the owner of his personal data, which means that he has rights over this information and can demand transparency from companies regarding its collection, storage, and use. This is a major change and, therefore, extremely important that everyone understands their role within LGPD. The purpose of this paper is to emphasize the principles of the General Law on Personal Data Protection, informing real cases of leakage of personal data and thus obtaining an understanding of the importance of gains that meet the interests of Internet users on the subject and its benefits to the entire Brazilian society.
Francesco Clavorà Braulin (ZEW – Leibniz Centre for European Economic Research) has posted “The Effects of Personal Information on Competition: Consumer Privacy and Partial Price Discrimination” on SSRN. Here is the abstract:
This article studies the effects of consumer information on the intensity of competition. In a two dimensional duopoly model of horizontal product differentiation, firms use consumer information to price discriminate. I contrast a full privacy and a no privacy benchmark with intermediate regimes in which the firms target consumers only partially. No privacy is traditionally detrimental to industry profits. Instead, I show that with partial privacy firms are always better-off with price discrimination: the relationship between information and profits is hump-shaped. Consumers prefer either no or full privacy in aggregate. However, even though this implies that privacy protection in digital markets should be either very hard or very easy, the effects of information on individual surplus are ambiguous: there are always winners and losers. When an upstream data seller holds partially informative data, an exclusive allocation arises. Instead, when data is fully informative, each competitor acquires consumer data but on a different dimension.