An End-to-End Pipeline from Law Text to Logical Formulas

We propose a pipeline for converting natural English law texts into logical formulas via a series of structural representations. Text texts are first parsed using a formal grammar derived from light-weight annotations. An intermediate representation called assembly logic is then used for logical interpretation and supports translations to different back-end logics and visualisations. The approach, while rule-based and explainable, is also robust: it can deliver useful results from day one, but allows subsequent refinements and variations

A Logic for Statutes

Case-based reasoning is, without question, a puzzle. When students are taught to “think like lawyers” in their first year of law school, they are taught case-based common-law reasoning. Books on legal reasoning are devoted almost entirely to the topic. How do courts reason from one case to the next? Is case-based reasoning reasoning from analogy? How should case-based reasoning be modeled? How can it be justified? In contrast, rule-based legal reasoning (as exemplified in much statutory reasoning) is taken as simple in legal scholarship. Statutory interpretation—how to determine the meaning of words in a statute, the relevance of the lawmakers’ intent, and so forth—is much discussed, but there is little treatment of the structure of statutory reasoning once the meaning of the words is established. Once the meaning of terms is established, statutory reasoning is considered, roughly speaking, to be deductive reasoning. This Essay examines the structure of statutory reasoning after ambiguities are resolved and the meaning of the statute’s terms established. It argues that standard formal logic is not the best approach for modeling statutory rule-based reasoning. Rather, the Essay argues, using the Internal Revenue Code and accompanying regulations, judicial decisions, and rulings as its primary example, that at least some statutory reasoning is best characterized as defeasible reasoning—reasoning that may result in conclusions that can be defeated by subsequent information—and is best modeled using default logic. The Essay then addresses the practical and theoretical benefits of this alternative understanding of rule-based legal reasoning

Catala: A Programming Language for the Law

Law at large underpins modern society, codifying and governing many aspects of citizens' daily lives. Oftentimes, law is subject to interpretation, debate and challenges throughout various courts and jurisdictions. But in some other areas, law leaves little room for interpretation, and essentially aims to rigorously describe a computation, a decision procedure or, simply said, an algorithm. Unfortunately, prose remains a woefully inadequate tool for the job. The lack of formalism leaves room for ambiguities; the structure of legal statutes, with many paragraphs and sub-sections spread across multiple pages, makes it hard to compute the intended outcome of the algorithm underlying a given text; and, as with any other piece of poorly-specified critical software, the use of informal language leaves corner cases unaddressed. We introduce Catala, a new programming language that we specifically designed to allow a straightforward and systematic translation of statutory law into an executable implementation. Catala aims to bring together lawyers and programmers through a shared medium, which together they can understand, edit and evolve, bridging a gap that often results in dramatically incorrect implementations of the law. We have implemented a compiler for Catala, and have proven the correctness of its core compilation steps using the F* proof assistant. We evaluate Catala on several legal texts that are algorithms in disguise, notably section 121 of the US federal income tax and the byzantine French family benefits; in doing so, we uncover a bug in the official implementation. We observe as a consequence of the formalization process that using Catala enables rich interactions between lawyers and programmers, leading to a greater understanding of the original legislative intent, while producing a correct-by-construction executable specification reusable by the greater software ecosystem

A platform-based Natural Language processing-driven strategy for digitalising regulatory compliance processes for the built environment

The digitalisation of the regulatory compliance process has been an active area of research for several decades. However, more recently the level of activities in this area has increased considerably. In the UK, the tragic incident of Grenfell fire in 2017 has been a major catalyst for this as a result of the Hackitt report’s recommendations pointing a lot of the blame on the broken regulatory regime in the country. The Hackitt report emphasises the need to overhaul the building regulations, but the approach to do so remains an open research question. Existing work in this space tends to overlook the processing of actual regulatory documents, or limits their scope to solving a relatively small subtask. This paper presents a new comprehensive platform approach to the digitalisation of the regulatory compliance processing. We present i-ReC (intelligent Regulatory Compliance), a platform approach to digitalisation of regulatory compliance that takes into consideration the enormous diversity of all the stakeholders’ activities. A historical perspective on research in this area is first presented to put things in perspective which identifies the challenges in such an endeavour and identifies the gaps in state-of-the-art. After enumerating all the challenges in implementing a platform-based approach to digitalising the regulatory compliance process, the implementation of some parts of the platform is described. Our research demonstrates that the identification and extraction of all relevant requirements from the corpus of several hundred regulatory documents is a key part of the whole process which underlies the entire process from authoring to eventually compliance checking of designs. Some of the issues that need addressing in this endeavour include ambiguous language, inconsistent use of terms, contradicting requirements and handling multi-word expressions. The implementation of these tools is driven by NLP, ML and Semantic Web technologies. A semantic search engine was developed and validated against other popular and comparable engines with a corpus of 420 (out of about 800) documents used in the UK for compliance checking of building designs. In every search scenario, our search engine performed better on all objective criteria. Limitations of the approach are discussed which includes the challenges around licensing for all the documents in the corpus. Further work includes improving the performance of SPaR.txt (the tool created to identify multi-word expressions) as well as the information retrieval engine by increasing the dataset and providing the model with examples from more diverse formats of regulations. There is also a need to develop and align strategies to collect a comprehensive set of domain vocabularies to be combined in a Knowledge Graph

JURI SAYS:An Automatic Judgement Prediction System for the European Court of Human Rights

In this paper we present the web platform JURI SAYS that automatically predicts decisions of the European Court of Human Rights based on communicated cases, which are published by the court early in the proceedings and are often available many years before the final decision is made. Our system therefore predicts future judgements of the court. The platform is available at jurisays.com and shows the predictions compared to the actual decisions of the court. It is automatically updated every month by including the prediction for the new cases. Additionally, the system highlights the sentences and paragraphs that are most important for the prediction (i.e. violation vs. no violation of human rights)