In-house legal teams (and as a result, the external lawyers they instruct) are under ever increasing pressure to produce high quality results within less time and at less cost. Some are looking to lawtech –technology-enabled processes and software – to find ways to increase productivity whilst driving greater efficiencies within their teams. The hype around lawtech comes with a lot of jargon which can be bewildering for those unfamiliar with the subject. To add to the confusion, there is often no precise definition for some of the terminology and the exact meaning of certain terms may be debated or in flux. This article briefly explains some frequently used jargon to assist readers to navigate the fast changing world of legal technology.
Artificial intelligence (AI)
AI is a field of computer science that includes machine learning, natural language processing, speech processing, expert systems, robotics, and machine vision. Many people assume that AI means Artificial General Intelligence (AGI) – that is, intelligence of a machine which performs any intellectual task as well as, or better than, a human can perform it. Or to put it another way, AGI is AI that can meet the so-called “Turing Test”: a machine’s ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. In reality, we are some way off the emergence of AGI, although we already benefit from AI which is itself designed by AI. AI will impact most, if not all, industry sectors – including law – in significant and possibly highly disruptive ways. Further resources about AI, including analysis of ethical and legal risks, are available on our website http://www.aitech.law.
A type of AI connoting automating decision-making using programming rules and, in some cases, training data sets. Human subject-matter experts can provide feedback on results as part of a training process. Machine learning can adapt its programming based on the training process and feedback, and the data can be represented by various graph and network structures. For example, an artificial neural network (ANN) or neural net is a system designed to process information in a way that is inspired by the framework of biological brains. Machine learning differs from automated decision-making based on conditional programming rules which follow pre-programmed “if-then” decision trees. Machine learning can now be seen in the context of contractual analysis (e.g. during a due diligence exercise), where lawyers teach the software to analyse contractual language and identify patterns or anomalies in contractual terms regardless of how they are phrased.
Natural language processing
An AI application which derives meaning, context, or sentiment in textual data or conversations with humans using grammars and graph structures.
The process of sorting through and manipulating large, complex, unstructured data sets to identify patterns and establish relationships in order to extract useful inferences or solve problems through data analysis. Often a foundation for AI/machine learning and the basis of Big Data or predictive analytics.
A branch of advanced data analytics that uses techniques including statistics, predictive modelling, machine learning and data mining to analyse data in order to make predictions about the future. Some companies offer predictive analytics software as a tool for predicting the likelihood of certain legal arguments being successful in certain courts, and before certain judges, relative to the type of case.
Rule-based software that automates the drafting of legal documents using rules and decision trees. At its simplest, document automation software combines a library of electronic templates with a pre-set question and answer and/or data-entry interface. Language is included or excluded based on the user’s answers, resulting in a document that is customised for a particular purpose or transaction.
Robotic process automation (RPA)
A type of AI software programme that utilises machine learning to automate high-volume, repeatable processes or tasks that previously required a human to perform. RPA is different from standard automation as RPA software can be trained by demonstrating the steps in a process rather than by using code-based programming. RPA software interacts with the process in question (often another computer application) in the same way a human user would. This makes it more adaptable and more easily used by human end users. In a recent report on innovation in law, the Law Society highlighted various areas where RPA software could be used, including: Land Registry checks, populating Ministry of Justice forms, employment tribunal preparation, conveyancing processing and data room administration.
Technology assisted review
Encompasses many forms of electronic document review technology including predictive coding (which uses algorithms to identify relevant documents), visual analytics (communication mapping and topic grouping), and keyword and concept searching. This is an area of technology that has already been extensively adopted by the legal industry (e.g. in e-disclosure exercises), but which continues to develop in sophistication.
Software applications that deploy a blockchain. A blockchain is simply a digital record (ledger) of transactions that is distributed – i.e. identical copies of the ledger are maintained on multiple computer systems. Originally derived from the technology underpinning cryptocurrencies (such as Bitcoin). Further resources about blockchain technology and Smart Contracts are available on our website.
A set of contractually binding promises in digital form, and which also includes the protocols for automatically performing those promises. Smart Contracts typically rely on blockchain technologies. For further information about Smart Contracts see our article on Arbitrating Smart Contract disputes.