The Rise of Legal Technology
Based on the Boston Consulting Group en Bucerius Law School categorization of legal technologies, we identify the following two major categories of currently applied technological solutions, in order of complexity and industry impact:
These technologies are focused on facilitating digitalization and optimizing back-office work and case-management, such as cloud storage tools, cybersecurity solutions and legal collaboration platforms. The latter can be divided into
- Meeting platforms where legal clients and legal service providers can meet. An example of a Belgian website is Jureca . Jureca is an online platform that helps people find the right legal solution. Effective legal advice is provided by affiliated attorneys who will guide you personally. A big advantage of using websites such as Jureca is that the costs of asking legal advice are much lower than when people would go see a lawyer in real life.
- Online dispute resolution (ODR) platforms that use technology to facilitate the resolution of all kinds of disputes between parties. In the first place, it is used for negotiation, mediation and arbitration . By using online dispute resolution, parties can avoid costly and time-consuming procedures. An example is The European ODR platform that was created in 2013 by the European Commission to make online shopping safer and fairer by providing access to appropriate dispute resolution tools . A complaint can be submitted by both consumer and entrepreneur. A neutral third party reaches a verdict which helps consumers and traders settle disputes in a non-confrontational way. Another example is “claim it”  which is an online Belgian platform where people can claim a compensation from airlines when their flight is cancelled or delayed. Once the claim for compensation has been validated, a specialist team will transfer a demand for payment to the airline as a the compensation of the cancellation or delay of the flight. The passengers only have to pay a commission to “claim it” when they receive a compensation from the airline so they do not bear any financial risk.
- Online knowledge hubs that centralize and provide information, intelligence and content about legislation, jurisprudence, legal doctrine and templates. In Belgium, websites like Jura, Jurisquare and Stradalex are currently the most well-known legal tech “Knowhow” websites.
These advanced solutions refer to chatbots and lawyer bots that support or even replace legal professionals in the execution of currently fairly easy and repetitive legal tasks in transaction and litigation cases. By the application of legal artificial intelligence, machines can perform tasks that are typically done by paralegals or young associates at law firms . The best-known lawyer bot is “Ross”. Ross is legal research software that uses artificial intelligence to help thousands of U.S. lawyers  and is marketed as “the world’s first artificially intelligent attorney.” 
Andrew Arruda, chief executive of Ross Intelligence, states:
“ROSS surfaces relevant passages of law and then allows lawyers to interact with them. Lawyers can either enforce ROSS’s hypothesis or get it to question its hypothesis,” Andrew Arruda also explains that human lawyers will not lose their job to robots: “With ROSS, lawyers can focus on advocating for their client and being creative rather than spending hours swimming though hundreds of links, reading through hundreds of pages of cases looking for the passages of law they need to do their job.”
Another famous lawyer bot is an American app named “DoNotPay” that started off as an app for contesting parking tickets. Now it can be used for different types of legal issues. The chatbot works by asking the person some basic questions about their situation and who they would like to sue. The app will then draw up the documents that the person needs to send to the courthouse to become a plaintiff, and will generate a script to read from if they need to attend in person . A big advantage of an app like “DoNotPay” is that it’s not creating any more legal rights for anyone, it’s just educating them about the rights they already have.
An example in Belgium is a chatbot named “Lee & Ally” (by De Juristen) to whom the consumer can ask legal questions. The more questions this chatbot gets asked, the smarter it gets. This is an example of machine learning. Another example is “Le BonBail” . Le Bonbail is a website that provides rental contract that were drafted by lawyers specialised in real estate law for free.
Legal Technology Adoption: EU, USA and China.
European law firms, and especially UK law firms, have adopted legal technology at a faster pace than U.S. law firms . This also the conclusion that can be drawn from the results generated by Trensition’s trend analytics engine, as can be seen in the second graph below.
In the USA, law firms still seem to be profitable with the usage of the billable hour, so there is little motivation to move away from this established model. The focus of junior lawyers is to work a lot of hours and to spend less attention to maximizing efficiency by using legal tech. However, lately, large American law firms seem to have realised the usefulness of legal technology.
The Chinese market of legal technology is growing rapidly to meet the rising needs of its people for accessible and affordable legal services . One of the biggest factors that drives the explosion of legal technology in China is the governmental support. Digital transformation is one of the top priorities of the Chinese government.
Algorithms can not think “outside the box”; the currently available legal technology is suitable for finding legal sources and even for decision support in policy and in the jurisprudence. However, decision-making should still be done by humans because the ability to have a social understanding and empathy is not something that computer programs can do.
- Only a very small percentage of jurisprudence is published. As a result, it is not yet possible to build programs that can make a right judgement . The Directive on open data and the re-use of public sector information entered into force on 16 July 2019 . This directive replaces the Public Sector Information Directive, also known as the ‘PSI Directive’. The PSI Directive has been in force since the end of 2003. This Directive obliges Member States to make data, such as case law, available to citizens. However, to date, only a very small fraction of case law is published online, which makes it not only difficult for people to find information about a topic, but also doesn’t provide a good resource to build programs. The usage and growth of artificial intelligence in the legal field depends mainly on the quality and quantity of these resources.
- Lawyers, legal experts, judges, paralegals will all lose their job if machines can take the place of people. This does not appear to be true: machines will not be replacing lawyers or other legal experts: “Rather, pattern recognition technology combined with machine learning complements the human skillset. Machines may alleviate the drudge work but this just makes the lawyer even more central to the process.”  Machine learning will support lawyers to do their work faster en more thorough. The lawyer will have more time to deal with the important parts of their cases and will have to spend less time on research or other tasks that can be done by machines.
A glimpse into the future
Imagine a machine capable to predict who will expose criminal activity, and where and when it will happen. Welcome to the world of predictive policing, an emerging paradigm (as can be seen in the graph below, generated by Trensition’s trend analytics engine) that builds on the premise that it is possible to predict when and where crimes will occur again in the future by using big data and predictive analytics. Predictive policing methods can be divided in four categories:
- predicting crimes,
- predicting offenders,
- predicting perpetrator’s identities and
- predicting victims of crimes.
Despite the promising prospects of predictive policing, for example a more efficient and effective deployment of resources and the rapid identification of individuals that potentially will be involved in an act of crime – either as victim or offender, the technology is still too immature for real-life applications :
- Most of the models used by predictive technology are data driven instead of theory driven. This can have major implications related to the introduction of bias or too much emphasis on correlations, instead of causality.
- WIth a lack of transparency and understanding of predictive models, accountability problems might occur: who is responsible for decision-making when there is full reliance on predictive algorithms?
- Using a mode of predictive policing for profiling may result in stigmatizing individuals and groups which is a form of discrimination based on algorithms.
- Concerns regarding privacy and ethics: can the information on social media be used for profiling? What about GDPR?