New Digital Case Law Service to Further Transform UK Litigation


Time to read

4 Minutes


Amanda Chaboryk
Disputes and Litigation Data Lead at Norton Rose Fulbright LLP
Adam Sanitt
Head of Disputes Knowledge, Innovation and Business Support, Norton Rose Fulbright LLP
David Wilkins
E-Disclosure Technical Lead at Norton Rose Fulbright LLP

The National Archives (‘NA’) made court judgments in England and Wales from the High Court, the Court of Appeal, the Supreme Court, and tribunal decisions from the Upper Tribunals freely available in April. This serves as the new official route for the online publication of court judgments and is the first time that judgments have been published under a clear copyright license. The NA are now managing the preservation, storage and publication of these court and tribunal judgments, which may be found through their free ‘Find Case Law’ service on their website. As the documents are published using the Legal Document Mark-up Language (an international open standard), they are machine-readable, which facilitates further processing and computational analysis. This is an opportunity to apply legal data analytics and machine learning to court judgments in the UK, as has been widely done in the US for many years. 

Machine Learning & AI

Machine learning (‘ML’) is a subset of Artificial Intelligence (‘AI’), which enables computers to perform some tasks traditionally completed by humans by automating decision-making, learning and recognition. Machine learning starts with data – computers use sets of rules (algorithms) to analyse data and recognize patterns. Once data is analysed through the application of these rules, computers ‘learn’ patterns and extract insights from the data.

ML is already widely used in the litigation industry for eDiscovery. In a disputes practice, eDiscovery specialists are primarily concerned with identifying disclosable documents and key evidence from a large volume of data provided by a client. Here, the original data is largely unstructured: emails, office documents, text messages, audio transcriptions and so on. As such, a key step in every case is the normalisation and structuring of this data, alongside its metadata (such as titles, authors, dates, senders, recipients and relationships between different documents) so it can be analysed successfully.

Similarly, the wide range of different formats and structures in which UK judgments exist has been an obstacle to analysis. The large variance in the format and content structure of judgements makes the use of sophisticated ML tools challenging. A pre-structured database, where this information is already indexed and available, therefore presents a rich opportunity for analysis and categorisation using the same tools. The Ministry of Justice committed to standardising its approach to the publication of judgements, following recommendations made by the Legal Education Foundation in their 2019 Digital Justice Report.

Litigation Analytics

Opportunities for deploying ML tools in the litigation industry have greatly expanded beyond legal research and eDiscovery. An illustrative example is the development of litigation analytics. Determining a case’s prospect of success and quantifying the value of a claim requires years of acquired legal training and experience and extensive analysis of similar actions. Legal practitioners can supplement their experience and training with insights derived from the strategic use of quantitative predictions obtained from statistical patterns, data analytics and AI. Many AI systems utilize predictive analytics, a branch of advanced analytics that makes predictions about future outcomes from historical data, in combination with statistical modelling, ML, and data mining methods.

Legal analytics is the use of analytic tools (including ML), to interrogate and examine large legal data sets and make predictions. Litigation analytics are specifically becoming increasingly widespread, facilitated by growing volumes of publicly available data sets. In the UK, Solomonic, a litigation analytics platform, offers data powered litigation intelligence, enabled by tracking new claims on the court record in real time. Another dynamic litigation analytics provider is Lex Machina, which captures state court data every 24 hours using special data mining tools. The output is information on many aspects of the litigation – covering parties, law firms, judges and courts, damages awarded and even timing analytics. The platform notably includes ‘Outcome Analytics’, providing insight on case resolutions, damages, and remedies that can help provide an understanding of case conclusions.  In cleaning, structuring and tagging legal data, Lex Machina is computationally analysing the law. Incorporating litigation analytics into litigation strategy will be conducive to achieving better outcomes through data-driven decisions and placing legal decisions, into the arena as other quantifiable risk judgements.

Digitising Case Law 

The digitisation of case law involves the extraction and often reorganization of information, so that it can be harvested by computers. The primary benefit of a central digital database and effectively ‘digitising case law’, is that the judgments are in a format that can be easily processed by computers. Judgments contain very useful data points, including who won, claim values, the success of particular applications, hearing dates, and parties. Computational analysis, in the context of legal analytics, relates to the use of big data tools and computer science to collect and analyse unstructured data (such as judgements). To achieve this, the NA have adopted the international standard Legal Document Mark-Up Language (‘LegalDocML’). A ‘Mark-Up Language’ is a computer language, which uses machine-readable tags to define elements within a document that is otherwise human-readable. Benefits of LegalDocML include judicial documents that permit search, interpretation and visualization and ‘the creation of a common legal document standard for the interchange of parliamentary, legislative and judicial documents between institutions anywhere in the world’. It is of note however, that users seeking to run computational analysis across judgements and decisions need to apply for a ‘transactional license’. As explained by the NA, this license permits users such as academic researchers, LawTech companies and legal publishers ‘to process judgement as data, to provide website search services, compute statistics or computationally analyse the law’.


The UK serves as a major international centre for dispute resolution, with the English courts and independent judiciary serving a prominent role in the development of the law. The NA’s creation of the first publicly available government database of judgements is reflective of investment in modernising the procedures in the publication of judgements, creating a sustainable infrastructure for digital record keeping. The primary benefits include increased judicial transparency, better access to justice and an overall richer source of precedents to provide greater insight on how judges construe the law in practice. It will be telling to see what legal insights companies, legal publishers and academic researchers achieve by computationally analysing the judgments. Furthermore, the database can enable (subject to obtaining the correct license) litigation analytics and yield unique insights to inform litigation strategy.

Amanda Chaboryk is Disputes and Litigation Data Lead at Norton Rose Fulbright LLP
Adam Sanitt is Head of Disputes Knowledge, Innovation and Business Support, Norton Rose Fulbright LLP
David Wilkins is an E-Disclosure Technical Lead at Norton Rose Fulbright LLP


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