The Faculty has distributed Volume 15 Number 5 of the University of Cambridge Faculty of Law Legal Studies Research Paper Series on SSRN.
This issue includes the following articles:
Sarah Nouwen: “Law & Peace”: Law, in what sense? (9/2024)
Literature on ‘law and peace’ has grown substantially. One of the explanations may be the flexible way in which the concept ‘law’ has been used. This entry for the forthcoming Elgar Concise Encyclopedia on Law and Peace, edited by Louise Mallinder, Rachel Killean and Lauren Dempster, uses five bodies of scholarly work on ‘law and peace’ to illustrate divergences in the conceptualisation of ‘law’. It argues that when reading the literature on ‘law and peace’, it is important to ask the question ‘law, in what sense?’
Felix Steffek et al: The Cambridge Law Corpus: A Dataset for Legal AI Research (11/2024)
We introduce the Cambridge Law Corpus (CLC), a corpus for legal AI research. It consists of over 250 000 court cases from the UK. Most cases are from the 21st century, but the corpus includes cases as old as the 16th century. This paper presents the first release of the corpus, containing the raw text and meta-data. Together with the corpus, we provide annotations on case outcomes for 638 cases, done by legal experts. Using our annotated data, we have trained and evaluated case outcome extraction with GPT-3, GPT-4 and RoBERTa models to provide benchmarks. We include an extensive legal and ethical discussion to address the potentially sensitive nature of this material. As a consequence, the corpus will only be released for research purposes under certain restrictions.
Joana Ribeiro de Faria, Huiyuan Xie & Felix Steffek: Automatic information extraction from Employment Tribunal judgements using large language models (13/2024)
Court transcripts and judgments are rich repositories of legal knowledge, detailing the intricacies of cases and the rationale behind judicial decisions. The extraction of key information from these documents provides a concise overview of a case, crucial for both legal experts and the public. With the advent of large language models (LLMs), automatic information extraction has become increasingly feasible and efficient. This paper presents a comprehensive study on the application of GPT-4, a large language model, for automatic information extraction from UK Employment Tribunal (UKET) cases. We meticulously evaluated GPT-4’s performance in extracting critical information with a manual verification process to ensure the accuracy and relevance of the extracted data. Our research is structured around two primary extraction tasks: the first involves a general extraction of eight key aspects that hold significance for both legal specialists and the general public, including the facts of the case, the claims made, references to legal statutes, references to precedents, general case outcomes and corresponding labels, detailed order and remedies and reasons for the decision. The second task is more focused, aimed at analysing three of those extracted features, namely facts, claims and outcomes, in order to facilitate the development of a tool capable of predicting the outcome of employment law disputes. Through our analysis, we demonstrate that LLMs like GPT-4 can obtain high accuracy in legal information extraction, highlighting the potential of LLMs in revolutionising the way legal information is processed and utilised, offering significant implications for legal research and practice.
Maurice Chiodo, Henning Grosse Ruse-Khan, Dennis Müller, Lea Ossmann-Magiera & Herbert Zech: Regulating AI: A Matrix for Gauging Impact and its Legal Implementation (12/2024)
This paper presents new and alternative avenues for regulating AI, understood in the broad sense of state intervention that directly or indirectly affects how AI is developed and deployed. In doing so, it also identifies two new regulatory axes to be addressed - Big/Small AI Impact, and Big/Small AI Teams - and the challenges associated with designing tools that are effective and tailored to these axes. Against this background, our proposals in this paper thus fall into two broad categories:
1. Forms of strict liability, as a means to incentivise operators of Small AI systems (where direct regulation is unlikely to be effective, and often unnecessary) to take steps preventing or at least minimising harm resulting from their AI.
2. A mechanism of constant monitoring through random sampling with human testing and determination, as well as associated cumulative “penalty points”, to control the impact of Big AI systems on individuals and society before they cause large-scale, and in particular systematic, harm to society.
Our proposals aim to have the maximal possible impact on the behaviour of AI operators and on where and how AI is used, so as to prevent harm and encourage responsible development and use of AI. In our view, these proposals are implementable, policeable, enforceable, costeffective,and technologically-neutral; they do not focus on what the AI is, but rather what it does. We consider different legal tools and mechanisms for implementing our proposals, and conclude that while elements of existing tort liability regimes, platform regulation as well as the EU AI Act can partially be relied on, law makers should consider adopting more tailored rules on both liability and constant monitoring.
Quentin Schäfer: Reconsidering the Limits of EU Competition Law on the IP-Competition Interface (14/2024)
Key Points
- The number of intellectual property rights in the modern economy has given rise to intellectual property law overprotects inventions and creations to the detriment of those who seek to licences, particularly for follow-on innovation.
- EU competition law has expanded considerably over the last decades to become the default device for the resolution of the overprotection problem while the TRIPS agreement has marginalised compulsory licences outside intellectual property law.
- Due to its institutional framework, EU competition law is not well suited towards this broader role but is indispensable to provide access to confidential information, including know-how.
- Other IP overprotection concerns are better addressed by exercising the courts’ discretion to deny injunctive relief or changes to the substantive rules of intellectual property law.
Graham Virgo: The Propriety of Proprietary Estoppel: What Guest v Guest Reveals About the State of Equity (18/2024)
This paper considers the implications of the decision of the Supreme Court in Guest v Guest on the law of proprietary estoppel, with particular reference to what the decision reveals about the state of Equity and whether a claim in proprietary estoppel is defensible and how it relates to other parts of private law.
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