Law & Reg Tech Seminar

S\Webinars - 2021

We are very pleased to announce the 1st edition of the #LAW and REG TECH SEMINAR (online). The Seminar is one of our PRIN-funded activities (Research Grant 2017BAPSXF).

It is co-organized with the LUISS DREAM research center. Some of the meetings will be hosted by the Law Faculty of LUISS, in my "Innovation Law and Regulation" course.

The #First #Session 'Theory&Method' is theoretical and runs through February-beginning March 2021. It introduces to different methods through which rulemaking, regulation, governance, enforcement, case law can be studied, analysed, enforced and enhanced through #algorithmic #tools, such as AI, machine learning, NLP, network analysis. We will also address challenging theoretical questions, such as setting the boundaries among disciplines, like CARL (Computational Analysis of Law and Regulation) and Law & Tech, Empirical Legal Studies and Legal Tech.
The
#Second #Session (March-mid April) is our favorite and will focus on how algorithmic tools can #enhance #transparency and #information #duties. Starting from the (rather skeptical) behavioral take on this, we will address #information #standards, #darkpatterns, and contrast this with computational methodologies.
The #Third and last #Session (mid April-mid May) will focus on the contribution of CARL in specific sectors, such as #Antitrust, #Ethics and AI, #Privacy, #ConsumerProtection, #CriminalLaw, #DigitalHealth.

Theory & Method: The Computational Analysis of Law and Regulation

Michael Livermore

Edward F. Howrey Professor of Law at
University of Virginia School of Law (
CV)

18 Feb. 2021, 17.30-19 CET

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Computational Analysis of Law: Theory and Methods

#1


The digitization of legal texts and advances in artificial intelligence, natural language processing, text mining, network analysis, and machine learning have led to new forms of legal analysis by lawyers and law scholars.

This article provides an overview of how computational methods are affecting research across the varied landscape of legal scholarship, from the interpretation of legal texts to the quantitative estimation of causal factors that shape the law. Already, computational methods have facilitated important contributions in a diverse array of law-related research areas. As these tools continue to advance, and law scholars become more familiar with their potential applications, the impact of computational methods is likely to continue to grow.

Wolfgang Alschner

Professor of Computational analysis of law, University of Ottawa

25 Feb. 2021, 17.30-19 CET

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Computational Analysis of Law: Differences with Empirical Legal Studies, Legal Tech and Others

#2

The seminar will explore Computational Analysis of Law and Regulation as a methodology, by distinguishing it from traditional law techniques, that reach their conceptual and methodological limits, but also from other contiguous disciplines like Empirical Legal Studies.

How is Computational Law and Regulation (CARL) different from contiguous disciplines such as Empirical Legal Studies or Legal Tech? What may computer science add to the understanding of legal concepts? For instance, international law scholars have in the past drawn inspirations from economics, political science or sociology to enrich the study and our understanding of international law. The computational analysis of international law renders legal analysis scalable and empowers scholars to study international law in unprecedented depth and breadth. But what is it really new about CARL compared to existing techniques such as Empirical Legal Studies?....

Monica Palmirani

Professor of Legal Informatics, University of Bologna.

Co-chair, LegalDocML and LegalRuleML

4 March 2021, 17.30-19 CET

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Legislation by Algorithm

#3


For long, computational models have been applied to legal knowledge mainly to support open access, reusability, interoperability, searchability of legal content on the Internet.

The seminar will take that to the next level by focusing on two broad ways AI could contribute to the law-making process and legislative activities by fostering machine-readable formats already implemented in the past (like e.g., Akoma Ntoso and LegalRuleML).

Firstly, the use of AI may prove essential for legal drafting or legislative automation.

In this scenario it could help finding best practices, discover effective norms, good patterns of linguistic formulations, detect inconsistent norms and definitions, reveal implicit modifications and repeals, underline some unused acts, cluster similar regulations, correlate different norms, detect unexpected relationships, support legislative offices to achieve better quality of legislation.

Secondly, we will focus on how in practice AI can make the law computable by-design (Law by-design), help checking compliance with norms and easing implementation (think e.g., to smart contracts). In both cases, we will discuss the opportunities and risks associated with the use of AI for making legislation by algorithm the new paradigm. In particular, we will remark the need to preserve transparency of the algorithmic process, using a solid legal-theory methodology, a robust explicability processing to avoid creating a new black box capable to obscuring democratic and due process principles.

CARL and (Algorithmic) Information

Fabiana Di Porto

Professor of Law, University of Salento and LUISS Univ.
Head of Algorithmic Disclosure PRIN Research Project

11 March 2021, 17.30-19 CET

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Disclosure by Algorithm.
A Computational Analysis of the DSA and DMA

#4

Disclosures are redundant and ineffective. Has the time come to have algorithms produce disclosure norms?

We investigate the potential of Machine Learning tools in overcoming the failures of countless disclosures (e.g. privacy notices, terms and agreements, risk information) in truly informing addressees about the issues they want to know about.

We propose a data-driven, targeted and partially automated regulatory process to make rule-making more efficient and the resulting norms more effective.
After presenting our methodology, we show how this would apply in the context of the proposed
EU Digital Services Act and Digital Markets Act.

How are computational tools useful to scale the processing of rulemaking documents? Think to the many replies stakeholders hand in when participating to an EU consultations, like those on the DSA and DMA. Can algorithms support rulemakers devise what stakeholders' main opinions are, and how to translate them into actual norms? If so, can such tools be relevant in interpretation and implementation too?

LINK TO THE PAPER

Lior Strahilewitz

Professor of Law, University of Chicago Law School

18 March 2021, 17.30-19 CET

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Dark Patterns: Integrating Computational Methods into their Assessment

#5

Dark patterns have been with us for some time now. But “how effective are they?” That question is as unanswered as vital if we are to understand the magnitude of the problem and whether regulation is appropriate. The lack of published research and data about the successes and failures of the techniques seems to suggest that dark patterns work and generate profits for the many firms employing them....

...Strahilevitz presents the results of two unique large-scale experiments on census-weighted samples of many widely employed dark patterns, providing the first comparative evidence of how strikingly effective DP are in getting consumers to do what they would not do when confronted with more neutral user interfaces.

What is the difference with vivid images, colors, aggressive advertising and unfair commercial practice? Does "scale" of the digital environment make any difference? And if so, should regulation of DP be an option?

Doron Teichman

Professor of Law, Hebrew University of Jerusalem

25 March 2021, 17.30-19 CET

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Behavioral Analysis of Disclosure Duties

#6

What are the main normative implications of behavioral economics for legal policymaking? More specifically, what goals should the law seek to achieve, and what means should it use to achieve its goals? These two normative questions will be discussed in the framework of disclosure duties, contrasting traditional economic with behavioral analysis.

Is regulation of information by disclosure duties well founded from a behavioral perspective? What empirical evidence is supporting such rulemaking strategy?


CARL in Specific Sectors

Maurice Stucke &
Ariel Ezrachi

Professor of Law, Tennessee University, &
Professor of Competition Law, Oxford University

8 April 2021, 17.30-19 CEST

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Computational Analysis in Competition Law

#7

What can algorithmic tools add to traditional legal research in competition law? Maurice Stucke and Ariel Ezrachi purport that algorithms can be a problem for competition in many regards (like with tacit collusion), but they can also provide useful tools. For instance, they may be used to support competition agencies in detecting anticompetitive conduct.

To what extent could algorithms be employed in antitrust enforcement?


Thibault Schrepel

Utrecht and Stanford Unis

15 April 2021, 17.30-19 CEST

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Computational Antitrust

#8


Computational antitrust is a new domain of legal informatics which seeks to develop computational methods for the automation of antitrust procedures and the improvement of antitrust analysis.

How can agencies, policymakers, and market participants benefit from it?

Schrepel sets out a research agenda for the years ahead in view of providing answers to the challenges created by computational antitrust, and better understand its limits.

Sofia Ranchordàs

Professor of Law
LUISS Uni of Rome and Groningen Uni


23 April 2021, 11.45-12.30 CEST

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Future-Proof Approaches to Legislation

#9

Future proofing science, design, architecture, and technology entails the creation of solutions that are forward-looking, sustainable, resilient, and can adapt to complex challenges. In the last years, lawmakers have also become intrigued by the question whether laws and policies could also be future-proof. This question has become particularly relevant considering the rapid changes that characterize the digital age. The implementation of a future-proof approach to legislation has nonetheless remained overlooked in the legal literature. Future proofing law is a challenging task for legislators but a cautious forward-looking approach could ensure that legislation becomes more adaptable and flexible to innovation. Drawing on the interdisciplinary literature on future proofing, we suggest the broader employment of experimental legislation and regulatory impact assessments.

Jerry Spanakis &
Catalina Goanta

C.G. is Assnt. Professor of Law, Maastrich U
J.S. is Assnt. Professor of Data Science, Maastrich U

6 May 2021, 17.30-19 CEST

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Computational Methods in
Consumer Law

#10

FThe Internet is helping regular users make money in ways that did not exist 10 years ago, such as influencer marketing or ad revenue. These users (sometimes referred to as influencers or content creators) engage in cultural production, namely they create content on social media platforms. To monetize their activity, they often act as vehicles for hidden advertising. European law is lagging behind in terms of interpretations (e.g. of unfair commercial practices), of drawing lines between lawful and unlawful behavior relating to the failure of disclosing advertising.

How does the doctrine view the current European legal frameworks and interpretations, including proposed reforms in the Digital Services Act?
Can a digital (legal) ethnography view of how to do qualitative empirical research on social media monetization be provided?
What is the contribution of
Natural Language Processing to the measuring and monitoring of social media disclosures?

2021 S\Webinar calendar


past events

Rupprecht Podszun

Henrich Heine University
18.11.2020 hr 17-19 CET

Vernissage Law & Reg Tech Webinar 2020

Digital Services Act and Digital Markets Act. What Regulation for the Big Platforms?