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Legal Systems and Artificial Intelligence


The aim of this project is to assess the implications of the introduction of Artificial Intelligence (AI) into legal systems in Japan and the United Kingdom. The project is jointly funded by the UK’s Economic and Social Research Council, part of UKRI, and the Japanese Society and Technology Agency (JST), and involves collaboration between Cambridge University (the CBR, Computer Laboratory and Faculty of Law) and Hitotsubashi University, Tokyo (the Graduate Schools of Law and Business Administration).

The use of machine learning (ML) to replicate aspects of legal decision making is already well advanced. A number of ‘Legal Tech’ applications have been developed by law firms and commercial suppliers and are being used, among other things, to model litigation risk. Data analytics are informing decisions on legally consequential matters including probation, predictive policing and credit evaluation. The next step will be to use ML to replicate core functions of legal systems, including adjudication. At the same time there are already signs of push-back against the use of ML in the legal sphere. Critics point to the biases in current algorithmic decision making processes which systematically disadvantage the poor and minority groups. Concerns over the constitutionality of automating judicial processes prompted the passage Art. 33 of French Law 2019-222, which bars the use of personally identifiable data of judges and other court officials with a view to ‘evaluating, analyzing, comparing or predicting their professional performance, real or supposed’

Aims & Objectives

In this context there is an urgent need for informed debate over the uses of AI in the legal sphere. The project will advance this debate by:

  1. exploring stakeholders’ perceptions of the acceptability of AI-related technologies in the legal domain
  2. identifying and addressing legal and ethical risks associated with algorithmic decision making
  3. understanding the potential of, and limits to, the computational techniques underlying law-related AI.  


The project is organised through three work packages which will deploy, respectively, the methods of Horizon Scanning (WP1), and machine learning, deep learning, natural language processing, and computational linguistics (WPs 2 and 3).

WP1: Constructing Future Scenarios for the Uses of AI in Law: A Horizon Scanning Approach

Project leaders: Washida, Sumida, Deakin

The Horizon Scanning Method was developed principally by the Stanford Research Institute in the late 1960s. The method avoids the assumption that the future will tend to deviate from a linear extension of current circumstancesm, and attempts instead to develop more realistic predictions of the future by focusing on the collection and analysis of information that does not lie on the path of this linear extension. In implementing the Horizon Scanning approach we will firstly produce a database containing a range of information sources on the uses of AI in law, drawn from press reports and commentary and secondary academic literatures. The database will be used as the basis for discussion at a series of workshops. We will invite experts, researchers, corporate professionals and users across a broad range of fields of activity and different age ranges to take part in the workshops. Emergent scenarios will describe different possible combinations of advantages and risks stemming from the use of AI.

WP2: Computation of Complex Knowledge Systems: Law and Accounting

Project leaders: Deakin, Markou, Crowcroft, Singh, Cobbe, Shuku, Noma

This WP will consider whether the juridical reasoning underpinning employment status decisions can be visually represented using historical data from decided cases and if the outcomes of cases can be accurately predicted using a decision-tree comprised of nodes corresponding to relevant legal indicators. We will use Deep Learning and NLP to analyse legal decisions for latent or hidden variables that can help inform and refine the model. We will then explore how far the same techniques can be applied to the digitisation of knowledge systems used in accounting.

WP3 Predicting the outcome of dispute resolution: feasibility, factors and ethical implications.

Project leaders: Steffek, Xie, Yamamoto

This WP deals with the prediction of dispute outcomes and generally aims to advance understanding of the use of artificial intelligence in case outcome predictions. Analysis will be carried out on a large data set of English court cases. The dataset will be used to test different ML approaches to predicting dispute outcomes. The possibility of carrying out a parallel study using Japanese court data will be explored. In addition this WP will develop ethical guidelines for regulating Artificial Intelligence in dispute resolution’. The development of the guidelines will be supported by roundtable meetings with the partners the UK Ministry of Justice, the OECD Department on Access to Justice, leading representatives of the UK judiciary and LawTech firms.


The project began in January 2020 and a planning meeting and workshop was held in Cambridge in early March, with the participation of the Japanese team. Shortly afterwards lockdowns were initiated in both Cambridge and Tokyo and work on the project was formally paused for a three-month period. Research was resumed in the summer of 2020. Progress has been made with respect to each of the WPs.

In WP1, the collection of abstracts for use in the Horizon Scanning Method began in August. The first workshop, originally planned to take place in Cambridge in December 2020, has been postponed to the academic year 2022-23 and its final form and timing is under review.

In WP2 progress has been made in developing the conceptual framework for the work, and has resulted in a series of publications including an edited collection, Is Law Computable? Critical Reflections on Law and Artificial Intelligence, which was published by Hart/Bloomsbury in November 2020, and papers published in the Journal of Cross-Disciplinary Research in Computational Law and the Northern Ireland Legal Quarterly. In addition, substantial progress has been made on constructing a dataset of historical employment cases which will be used to test hypotheses concerning the long-run dynamics of legal change and the coevolution of law with social and economic development.

In WP3 work has been carried out on the dataset of English cases and the possibility of creating similar datasets of Japanese cases has been explored with relevant stakeholders. Progress has also been made in developing the ML and NLP methods which will be used to analyse the judicial data. As regards the English data set of court cases, the focus was on pre-processing the data set. Guidance for manually tagging selected cases has been produced and the manual tagging of the data set has been concluded.

Both WP2 and WP3 have organised multiple meetings between the British and Japanese sides, via zoom, to coordinate progress and ensure continuing cooperation notwithstanding the impossibility of meeting in person during the Covid emergency.


Deakin, S. and Markou, C. (2021) ‘Evolutionary law and economics: theory and method’ Northern Ireland Legal Quarterly, 72: 682-712.

Deakin, S. and Markou, C. (2022) ‘Evolutionary interpretation: law and machine learning’ Journal of Cross-Disciplinary Research in Computational Law online first

Project team

Project leader: Gerhard Schnyder, University of Loughborough London

Cambridge PI: Simon Deakin

Researcher: Louise Bishop

Project status


Project dates



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