Copyright as a constraint on creating technological value

Defence date: 8 January 2019Examining Board: Giovanni Sartor, EUI; Peter Drahos, EUI; Jane C. Ginsburg, Columbia Law School; Raquel Xalabarder, Universitat Oberta de Catalunya.How do we legislate for the unknown? This work tackles the question from the perspective of copyright, analysing the judicial practice emerging from case law on new uses of intellectual property resulting from technological change. Starting off by comparing results of actual innovation-related cases decided in jurisdictions with and without the fair use defence available, it delves deeper into the pathways of judicial reasoning and doctrinal debate arising in the two copyright realities, describing the dark sides of legal flexibility, the attempts to ‘bring order into chaos’ on one side and, on the other, the effort of judges actively looking for ways not to close the door on valuable innovation where inflexible legislation was about to become an impassable choke point. The analysis then moves away from the high-budget, large-scale innovation projects financed by the giants of the Internet era. Instead, building upon the findings of Yochai Benkler on the subject of networked creativity, it brings forth a type of innovation that brings together networked individuals, sharing and building upon each other’s results instead of competing, while often working for non-economic motivations. It is seemingly the same type of innovation, deeply rooted in the so-called ‘nerd culture’, that powered the early years of the 20th century digital revolution. As this culture was put on trial when Oracle famously sued Google for reuse of Java in the Android mobile operating system, the commentary emerging from the surrounding debate allowed to draw more general conclusions about what powers the digital evolution in a networked environment. Lastly, analysing the current trends in European cases, the analysis concludes by offering a rationale as to why a transformative use exception would allow courts to openly engage in the types of reasoning that seem to have become a necessity in cases on the fringes of copyright

AI in search of unfairness in consumer contracts : the terms of service landscape

Published online: 18 July 2022This article explores the potential of artificial intelligence for identifying cases where digital vendors fail to comply with legal obligations, an endeavour that can generate insights about business practices. While heated regulatory debates about online platforms and AI are currently ongoing, we can look to existing horizontal norms, especially concerning the fairness of standard terms, which can serve as a benchmark against which to assess business-to-consumer practices in light of European Union law. We argue that such an assessment can to a certain extent be automated; we thus present an AI system for the automatic detection of unfair terms in business-to-consumer contracts, a system developed as part of the CLAUDETTE project. On the basis of the dataset prepared in this project, we lay out the landscape of contract terms used in different digital consumer markets and theorize their categories, with a focus on five categories of clauses concerning (i) the limitation of liability, (ii) unilateral changes to the contract and/or service, (iii) unilateral termination of the contract, (iv) content removal, and (v) arbitration. In so doing, the paper provides empirical support for the broader claim that AI systems for the automated analysis of textual documents can offer valuable insights into the practices of online vendors and can also provide valuable help in their legal qualification. We argue that the role of technology in protecting consumers in the digital economy is critical and not sufficiently reflected in EU legislative debates.Francesca Lagioia has been supported by the European Research Council (ERC) Project “CompuLaw” (Grant Agreement No 833647) under the European Union’s Horizon 2020 research and innovation programme, and by the SCUDO project, within the POR-FESR 2014-2020 programme of Regione Toscana. Agnieszka Jabłonowska has been supported by the National Science Center in Poland (Grant Agreement UMO-2019/35/B/HS5/04444). This work has been supported by the Claudette (CLAUseDETecTEr) project, funded by the Research Council of the European University Institute and from the Bureau Européen des Unions de Consommateurs (BEUC)

Deep Learning for Detecting and Explaining Unfairness in Consumer Contracts

Consumer contracts often contain unfair clauses, in apparent violation of the rel- evant legislation. In this paper we present a new methodology for evaluating such clauses in online Terms of Services. We expand a set of tagged documents (terms of service), with a structured corpus where unfair clauses are liked to a knowledge base of rationales for unfairness, and experiment with machine learning methods on this expanded training set. Our experimental study is based on deep neural net- works that aim to combine learning and reasoning tasks, one major example being Memory Networks. Preliminary results show that this approach may not only pro- vide reasons and explanations to the user, but also enhance the automated detection of unfair clauses

GDPR Privacy Policies in CLAUDETTE: Challenges of Omission, Context and Multilingualism

The latest developments in natural language processing and machine learning have created new opportunities in legal text analysis. In particular, we look at the texts of online privacy policies after the implementation of the European General Data Protection Regulation (GDPR). We analyse 32 privacy policies to design a methodology for automated detection and assessment of compliance of these documents. Preliminary results confirm the pressing issues with current privacy policies and the beneficial use of this approach in empowering consumers in making more informed decisions. However, we also encountered several serious issues in the process. This paper introduces the challenges through concrete examples of context dependence, omission of information, and multilingualism

A corpus for multilingual analysis of online terms of service

We present the first annotated corpus for multilingual analysis of potentially unfair clauses in online Terms of Service. The data set comprises a total of 100 contracts, obtained from 25 documents annotated in four different languages: English, German, Italian, and Polish. For each contract, potentially unfair clauses for the consumer are annotated, for nine different unfairness categories. We show how a simple yet efficient annotation projection technique based on sentence embeddings could be used to automatically transfer annotations across languages

Cross-lingual Annotation Projection in Legal Texts

We study annotation projection in text classification problems where source documents are published in multiple languages and may not be an exact translation of one another. In particular, we focus on the detection of unfair clauses in privacy policies and terms of service. We present the first English-German parallel asymmetric corpus for the task at hand. We study and compare several language-agnostic sentence-level projection methods. Our results indicate that a combination of word embeddings and dynamic time warping performs best