The legal profession, the technology industry and the relationship between the two are in a state of transition. Computer processing power has doubled every year for decades, leading to an explosion in corporate data and increasing pressure on lawyers entrusted with reviewing all of this information.
Now, the legal industry is undergoing significant change, with the advent of machine learning technology fundamentally reshaping the way lawyers conduct their day-to-day practice. Indeed, whilst technological gains might once have had lawyers sighing at the ever-increasing stack of documents in the review pile, technology is now helping where it once hindered. For the first time ever, advanced algorithms allow lawyers to review entire document sets at a glance, releasing them from wading through documents and other repetitive tasks. This means legal professionals can conduct their legal review with more insight and speed than ever before, allowing them to return to the higher-value, more enjoyable aspect of their job: providing counsel to their clients.
In this article, we take a look at how this has been made possible.
The beginnings of legal technology
Practicing law has always been a document and paper-heavy task, but manually reading huge volumes of documentation is no longer feasible, or even sustainable, for advisors. Even conservatively, it is estimated that we create 2.5 quintillion bytes of data every day, propelled by the usage of computers, the growth of the Internet of Things (IoT) and the digitalisation of documents. Many lawyers have had no choice but resort to sampling only 10 per cent of documents, or, alternatively, rely on third-party outsourcing to meet tight deadlines and resource constraints. Whilst this was the most practical response to tackle these pressures, these methods risked jeopardising the quality of legal advice lawyers could give to their clients.
Legal technology was first developed in the early 1970s to take some of the pressure off lawyers. Most commonly, these platforms were grounded on Boolean search technology, requiring months and even years building the complex sets of rules. As well as being expensive and time-intensive, these systems were also unable to cope with the unpredictable, complex and ever-changing nature of the profession, requiring significant time investment and bespoke configuration for every new challenge that arose. Not only did this mean lawyers were investing a lot of valuable time and resources ‘training’ a machine, but the rigidity of these systems limited the advice they could give to their clients. For instance, trying to configure these systems to recognise ‘bespoke’ clauses or subtle discrepancies in language was a near impossibility.
Looking forward: machine learning today
Today, machine learning has become advanced enough that it has many practical applications, a key one being legal document review.
Machine learning can be broadly categorised into two types: supervised and unsupervised machine learning. Supervised machine learning occurs when a human interacts with the system – in the case of the legal profession, this might be ‘tagging’ a document, or categorising certain types of documents, for example. The machine then builds its understanding to generate insights to the user based on this human interaction.
Unsupervised machine learning is where the technology forms an understanding of a certain subject without any input from a human. For legal document review, the unsupervised machine learning will cluster similar documents and clauses, along with clear outliers from those ‘standards’. Because the machine requires no a priori knowledge of what the user is looking for, the system may indicate anomalies or ‘unknown unknowns’- data which no one had set out to identify because they didn’t know what to look for. This allows lawyers to uncover critical hidden risks in real time.
It is the interplay between supervised and unsupervised machine learning that makes technology like Luminance so powerful. Whilst the unsupervised part can provide lawyers with an immediate insight into huge document sets, these insights only increase with every further interaction, with the technology becoming increasingly bespoke to the nuances and specialities of a firm.
This goes far beyond more simplistic contract review platforms. Machine learning algorithms, such as those developed by Luminance, are able to identify patterns and anomalies in a matter of minutes and can form an understanding of documents both on a singular level and in their relationship to each another. Gone are the days of implicit bias being built into search criteria, since the machine surfaces all relevant information, it remains the responsibility of the lawyer to draw the all-important conclusions. But crucially, by using machine learning technology, lawyers are able to make decisions fully appraised of what is contained within their document sets; they no longer need to rely on methods such as sampling, where critical risk can lay undetected. Indeed, this technology is designed to complement the lawyers’ natural patterns of working, for example, providing results to a clause search within the document set rather than simply extracting lists of clauses out of context. This allows lawyers to deliver faster and more informed results to their clients, but crucially, the lawyer is still the one driving the review.
With the right technology, lawyers can cut out the lower-value, repetitive work and focus on complex, higher-value analysis to solve their clients’ legal and business problems, resulting in time-savings of at least 50 per cent from day one of the technology being deployed. This redefines the scope of what lawyers and firms can achieve, allowing them to take on cases which would have been too time-consuming or too expensive for the client if they were conducted manually.
Machine learning is offering lawyers more insight, control and speed in their day-to-day legal work than ever before, surfacing key patterns and outliers in huge volumes of data which would normally be impossible for a single lawyer to review. Whether it be for a due diligence review, a regulatory compliance review, a contract negotiation or an eDiscovery exercise, machine learning can relieve lawyers from the burdens of time-consuming, lower value tasks and instead frees them to spend more time solving the problems they have been extensively trained to do.
In the years to come, we predict a real shift in these processes, with the latest machine learning technology advancing and growing exponentially, and lawyers spending more time providing valuable advice and building client relationships. Machine learning is bringing lawyers back to the purpose of their jobs, the reason they came into the profession and the reason their clients value their advice.
James Loxam, CTO, Luminance