This year, the judges have selected a shortlist of seven for the ALPSP Awards for Innovation in Publishing.  Each finalist will be invited to showcase their innovation to industry peers on 14 September on the opening day of the ALPSP 2022 Conference in Manchester. 
The winners will be announced at the Awards Dinner on Thursday 15 September.In this series, we learn more about each of the finalists.

Case Genie, built by 67 Bricks for ICLR, is an AI-powered search tool using programmed intuition to revolutionise case building by surfacing the ‘unknown unknowns’.

Tell us about your organization

67 Bricks is a technology consultancy specialising in supporting scholarly publishers to digitally transform. We’re best known for building great software to unlock publishers’ content and data and allow them to be more flexible in the way they meet customer needs - we’ve twice won the OpenAthens Best Publisher UX award for our content platform builds, first for Emerald in 2019, and this year for De Gruyter. Beyond that, we offer a full technology consultancy service - whether that’s through making better use of publishers’ internal data, developing bespoke software to solve specific problems, or guiding companies through a program of complete digital transformation - we’re here to help.

This entry is submitted with the support of the Incorporated Council of Law Reporting for England and Wales (ICLR). As well as the official Law Reports, ICLR publishes The Weekly Law Reports, The Industrial Cases Reports, The Business Law Reports, The Public and Third Sector Law Reports and the Consolidated Index to leading law reports. ICLR was established in 1865 by members of the legal profession and its mission is as important today as ever and explains why they are universally regarded as the most authoritative source of English case law.

 

What is the project/product that you

submitted for the Awards?

As an increasing amount of legal case law is moved online, the question for barristers, law students and solicitors is ‘how do I find the cases I need that I don’t know about’? Enter Case Genie. Case Genie represents a real evolution to the way legal cases are constructed - taking the valuable database of case reports held by ICLR and creating a way to search for relevant cases quickly and intelligently. Using Artificial Intelligence (AI) in the form of Natural Language Processing (NLP) to analyse a user’s own document in order to identify the legal concepts and issues in it, the search tool then uses programmed intuition to suggest relevant cases from the ICLR database. Early results have been extremely positive and Case Genie has been named “a potential game-changer” by users.

 

Tell us a little about how it works and the team behind it

 

ICLR made

its Case Reports and Indices available online over a decade ago. ICLR.4 makes

it easy to see which important cases have been Affirmed or Overruled (there

are, in fact, seventeen variations on these!). The contents of the Case Reports

can also be searched, and this has been an important resource for judges,

barristers, solicitors and magistrates.

 

What Case

Genie adds to this, is the ability to prime the standard search with a starting

document. As part of preparing a case, solicitors and barristers create formal

legal documents (such as skeleton arguments) that summarise the case and its

arguments. Such documents can be uploaded to ICLR.4, either whole or selected

extracts, to find existing cases that are conceptually similar. These similar

cases can be further expanded to include linked cases (important cases that

cite or are cited by the similar cases). The results of these can be filtered

using more traditional search facilities in ICLR.4. The aim is to help the

lawyer find cases that they might not otherwise have considered.

 

In addition,

Case Genie finds cases cited in the uploaded document and lists them with their

subsequent treatment (Approved/Overruled etc). This can save the lawyer

significant time.

 

Superadded

to those, Case Genie facilitates finding similar paragraphs. When reading the

judgment section of a Case Report, the user can click on a paragraph to find

paragraphs from other judgments that are conceptually similar.

 

The team

behind the tool is made up of both software developers and publishing

consultants, who worked deeply with ICLR to really understand customer needs.

Feedback from users was that the site’s search functionality was great when you

knew what you were looking for, but the experience of finding content when you

didn’t know where to start was much harder. We set about testing ideas and

technical approaches to help users find ‘unknown unknowns’ when they were

working in unfamiliar areas. After lots of iteration and validation, we settled

on the ML/NLP approach as a way to leverage all of the valuable data and

editorial content ICLR has to make relevant content more accessible to users.

 

In what ways do you think it demonstrates innovation?

 

There are several

layers to this. Firstly, the tool uses unsupervised learning, which is crucial

for avoiding bias in the output. Supervised learning

uses example data that teach the ML algorithm how to predict a given outcome,

which has all of the pitfalls of bias. Usually, the data have come from

real-world decisions made by people, so it embodies the biases of those

decisions. Unsupervised learning, however, is not based on previous human

decisions. The models created by unsupervised learning map connections between

data, but do not try to predict a specific variable. Case Genie uses

unsupervised Machine Learning to build document embeddings for each Case Report

and judgment transcript. The only biases it embodies are those of the judges in

their language (but not their verdict or sentence), which are actually what we

are most interested in! Language use changes over time, so two cases from a

specific period are more likely to have high similarity if they cover similar

topics. This is beneficial because it means more recent cases are likely to

match new case material; whereas important, older cases will still surface in

the results when added as linked results. The goal of using NLP and ML in this

project is to surface surprising cases. We have worked very closely with ICLR

to validate the results of Case Genie, because it requires a very high level of

domain knowledge to determine whether a result is a surprising but good result;

or just noise.

 

We

have also had to work in new ways to ensure data privacy, as this was a very

important aspect of the development for ICLR. The architecture of ICLR.4 is

therefore built around the need to secure sensitive legal information in

ongoing cases. Uploaded document data and information derived from it is never

stored unencrypted. Two keys are required to unencrypt the data, one is stored

in the system database and is unique for each user; the other is transient and

created by the user’s browser for each user session. As soon as the original

document has been processed, it is deleted. Derived ephemeral documents are

deleted as soon as the initial processing pipeline has completed. The remaining

data, required to show the user the results of the processing, is available

only for the duration of the user’s session. For example, if they close their

browser and use a fresh log-in, they will not be able to access their own

results.

 

Finally,

the tool has enabled ICLR to innovate on their own business models. Case Genie

has switched the customer focus for ICLR from bigger institutions to smaller or

even individual users, providing additional value and expanding the potential

for ICLR to find new revenue streams in the future. This advances ICLR’s

mission to make case law accessible by supporting access for individual users.

As well as access, the tool also makes it easier for single individuals to

build a case using whatever resources they currently have since the heavy

lifting of searching for relevant cases is done automatically.

 

What are your plans for the future?

 

We

have built the system with ongoing scale and development in mind. We have

created a processing pipeline that is secure, can be built upon and is

scalable. Scalability is essential for a system like this because NLP can be

very processor intensive. We also have some large data indices that sit in

memory so that they are blisteringly fast. We adapted the architecture to fit

the changing needs as the system was built. The end result is that the system

can easily be scaled out to support a larger number of users and new use cases

for the machine learning model.

Read more about Case Genie and 67 Bricks. 

Visit the ALPSP Annual Conference 2022 Website for more details and to book your place. 

Authors: 

Will

Bailey, Head of Partnerships, 67 Bricks

Rhys

Parsons, Technical Lead, 67 Bricks

Social Media: 67 Bricks (@67Bricks) / Twitter