From BADs to BEDs. Algorithmic Disclosure Regulation. Theoretical aspects for empirical application

70 Pages Posted: 25 Jun 2020 Last revised: 16 Jul 2020

See all articles by Fabiana Di Porto

Fabiana Di Porto

University of Salento ; Law Faculty, Hebrew University; Luiss Guido Carli University

Date Written: June 23, 2020

Abstract

Amidst the growing skepticism surrounding transparency measures, this paper supports that disclosure regulation can play a role in online transactions, if it is designed through algorithms. This requires solving several theoretical legal challenges and bridging technical solutions, making this article contribute to the Law&Tech scholarship.

The contribution of this article is threefold. First, it provides a review of an emerging scholarship: the Law&Technology (Law&Tech), which is distinct from the Technology or Innovation Law, the Legal Tech and Empirical Legal Studies. Law&Tech is a new research field that uses tools from Natural Language Processing (NLP) and Machine Learning (ML) to investigate on legal topics. We classify it around four research fields: (i) analysis, (ii) interpretation (iii) application and enforcement, (iv) enhancement. (Part One) Second, the article draws on the fourth (iv) future-oriented sub-category of the Law&Tech literature to demonstrate that research on how algorithms could contribute to enhance the rule- making of disclosure regulation is still limited. It therefore advances the current knowledge on how that would be possible.

Third, it presents a new (two-phase) model to draft effective disclosure regulation. In both phases, algorithmic technologies are used, making this contribution fit the Law&Tech scholarship. In phase one, we suggest using Natural Language Processing and Machine Learning technologies to first combine Rules and Industry disclosures (the de iure and de facto disclosures), and then grade them to select those that ‘fail’ the least: the BADs, or Best Available Disclosures. (Part Two)

In phase two, we propose using regulatory sandbox to test the BADs and get to the BEDs (or Best Ever Disclosures). Technically speaking, the BEDs would: be adaptive to the addressees’ informational needs (i.e. feed-in through behavioral data); targeted at diversified groups of addressees; cheap to implement. From a legal point of view, testing the messages in the sandbox before implementation serves to grant due process (transparency and participation) of algorithmic rule making; but also to increase BEDs’ proportionality (least possible burdensome solution for recipients) (Part Three).

Keywords: Law, disclosure duties, disclosure regulation, natural language processing, machine learning, data, competition, regulatory sandbox

JEL Classification: K00, K12

Suggested Citation

Di Porto, Fabiana, From BADs to BEDs. Algorithmic Disclosure Regulation. Theoretical aspects for empirical application (June 23, 2020). Hebrew University of Jerusalem Legal Research Paper 20-18, Available at SSRN: https://ssrn.com/abstract=3633847 or http://dx.doi.org/10.2139/ssrn.3633847

Fabiana Di Porto (Contact Author)

University of Salento ( email )

Via per Monteroni
Lecce, Lecce 73100
Italy

Law Faculty, Hebrew University ( email )

Mount Scopus
Mount Scopus, IL 91905
Israel

Luiss Guido Carli University

Viale Romania
Rome, Roma 00100
Italy

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