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.)

Jack Tsen-Ta Lee and Phang Hsiao Chung (Attorney-General’s Chambers, Singapore) have posted “Report on Civil Liability for Misuse of Private Information” (Simon Constantine (ed), Singapore: Law Reform Committee, Singapore Academy of Law, 2020) on SSRN. 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.)

Recommended.

Peter Salid (Harvard Law School) has posted “Machine Learning and Class Certification” on SSRN. Here is the abstract:

Class actions are supposed to allow plaintiffs to recover for their high-merit, low-dollar claims. But current law leaves many such plaintiffs out in the cold. To be certified, classes seeking damages must show that, at trial, “common” questions (those for which a single answer will help resolve all class members’ claims) will predominate over “individual” ones (those that must be answered separately as to each member). Currently, many putative class actions in important doctrinal areas—mass torts, consumer fraud, employment discrimination, and more—are regarded as uncertifiable for lack of predominance. As a result, even plaintiffs with valid claims in these areas have little or no access to justice. This state of affairs is exacerbated by a line of Supreme Court cases beginning with Wal-Mart Stores, Inc. v. Dukes. There, the Court disapproved of certain statistical methods for answering individual questions and achieving the predominance of common ones.

This Article proposes a first-of-its kind solution: A.I. class actions. Advanced machine learning algorithms could be trained to mimic the decisions of a jury in a particular case. Then, those algorithms would expeditiously resolve the case’s individual questions. As a result, common questions would predominate at trial, facilitating certification for innumerable currently-uncertifiable classes. This Article lays out the A.I. class action proposal in detail. It argues that the proposal is feasible; the necessary elements are precedented in both complex litigation and computer science. The Article also argues that A.I. class actions would survive scrutiny under Wal-Mart, though other statistical methods have not. To demonstrate this, the Article develops a new, comprehensive theory of the higher-order values animating Wal-Mart and its progeny. It shows that these cases are best explained as approving statistical proof only if it can deliver accurate answers at the level of individual plaintiffs. Machine learning can deliver such accuracy in spades.