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The emergence of artificial intelligence (AI) has the potential to revolutionise the practice of law. When we consider how emails changed the way we do law, it is not hard to believe that new technology will impact the legal industry. But what is AI? The term is thrown around a lot, but it can be confusing to understand what AI products are out there. This article will provide an overview of what AI actually is and illustrate how it impacts the legal industry.

A History of Artificial Intelligence

AI is a broad concept that refers to the intelligence of machines, in comparison to the natural intelligence of humans.  AI was a notion born in the post-war technological optimism of the 1950s. It grew from statistical and mathematical theories of the time in an attempt to mimic human capacities for problem-solving. Initially, people used AI to play checkers.

A few examples of AI that have become prevalent in our lives are: 

  • Gmail (through its email filters and smart replies);
  • Amazon’s Alexa; and 
  • Pandora Radio.

While AI is the initial and all-encompassing concept, machine learning and deep learning have also brought technological advances.

Machine Learning

Machine learning is a subset of AI that focuses on using computers and algorithms to allow systems to learn and improve from experience without someone having to explicitly program it. Entrepreneurs have increasingly used machine learning to help computers process camera images to detect shapes.

For example, in the way that self-driving cars (like Tesla’s) can identify people and ensure not to hit them.

Neural Networks & Deep Learning 

Neural networks are a subset of machine learning. They use a unique method of learning that (somewhat) mirrors the brain. Basic machine learning models improve from experience and get better at whatever their function is, but they do need some guidance. 

The algorithms in neural networks can determine on their own if a prediction is accurate or not. Neural networks can be ‘shallow’ in their structure (i.e. having less complexity) or ‘deep’ (having more complexity). When they are more complex, they are known as ‘deep learning’. Deep learning was popularised in 2012 when Google employee Andrew Ng trained a computer to find images of cats after processing cat videos from youtube.

A current example of Neural networks is Google’s Alpha Go. Alpha Go is a computer program that engineers built to play the ancient Chinese game of Go. The sheer volume of possible moves meant that it was not viable to calculate every possible move to find the best one. In a historic moment, Alpha Go famously defeated Go world number 1 Lee Sedol in 2016 by using smart algorithms that were able to detect the most powerful moves.

Current Impacts of AI on Law

On 14 January 2011, IBM’s supercomputer ‘Watson’ played Jeopardy! against the world’s two best players at the time. This was a successful demonstration of the ‘intelligence’ of computers, with IBM’s supercomputer winning the game. Watson’s engineers have now trained it to retrieve law-specific information as a method of supporting lawyers in their jobs. However, there are many other ways that AI can impact the legal profession.

Legal Research

Due to the complexity and ever-changing nature of the law, being on top of legal changes is integral for legal practitioners. However, doing legal research is one of the more time-consuming facets of the job. To combat this issue, companies like ROSS Intelligence have been developed. 

Based out or San Francisco, ROSS uses AI and natural language processing methods to deconstruct research queries framed in everyday language. This means that the software is able to:

  • interpret the meaning of the question;
  • break it down into its relevant parts; and 
  • begin to search the appropriate databases for the answers to these queries. 

In some way, this could be described as a highly advanced Google search engine for the law.

Lawyers have used this technology to shorten the time they spend on research and cut out the somewhat repetitive tasks of trawling websites and cases for information.


Another area that AI has been applied to is chatbots. A chatbot is a computer programme that imitates human conversation. Artificial intelligence makes it possible for chatbots to learn by discovering patterns in data when it takes the form of:

  • natural language processing; 
  • deep learning; and 
  • machine learning, 

Chatbots are catered to the public, rather than lawyers. These applications of AI are slightly more simplistic as they primarily serve as an interface between the public and simple pieces of legal information.

One example of this is the software called DoNotPay, which helps people appeal parking tickets. Since 2016, this software has helped to appeal over 150,000 parking tickets in the US and UK. 

In a similar way to how robots have drastically reduced headcount in factories, chatbots have the potential to impact headcount in any client facing industry, including the legal sector. This form of customer relations is unique in that chatbots are:

  • available 24/7;
  • require minimal upkeep costs to run; and 
  • able to provide clients with legal help at a moment’s notice. 

This means that lawyers’ jobs may be made easier by chatbots that are able to distil the primary concerns of a client through an AI-powered ‘initial consultation’.

Further, when augmented with the right technology, these chatbots may be able to inform the relevant lawyer of these points through an automatically generated and partially-filled advice document, thus drastically reducing the time needed to provide a particular client legal advice. 

Big Data Analytics

Big data analytics is the use of advanced analytic techniques to examine large amounts of data and uncover hidden patterns, trends, correlations and preferences. With AI, organisations can analyse data and identify insights almost immediately. 

Big data analytics comes in three primary forms

The first form of big data analytics is descriptive analytics. Descriptive analytics integrates machine learning and natural language processing to process large amounts of legal information and derive useful insights. This might include analysing the behavioural tendencies of litigation participants or identifying long-term trends in the law.

You can then use this information to construct stronger and informed strategies for:

  • winning cases;
  • predicting litigation costs; and 
  • making decisions on whether or not to settle.

The second form of big data analytics is predictive analytics. This creates holistic profiles of a given group or individual and predicts behaviour. You can then use this information to understand the tendencies of juries or judges and deliver information accordingly. Legal tech startups have already sprung up around this area.

For example, the US-based Judge Analytics provides a comprehensive statistical analysis of every judge across every US court.

The third form of big data analytics is prescriptive analytics, which offers advice based on probable outcomes. Prescriptive analytics recommends specific courses of action and informs on the likely outcome. This technology tracks the outcomes of legal decisions and refines its recommendations. Effectively, prescriptive analytics is another layer of information processing that was once done manually to garner insights into the law.

Key Takeaways 

AI is an extremely powerful and exciting field. It’s only going to become more critical and ubiquitous moving forward, and will certainly have an impact on the legal industry. Neural networks and deep learning are some of the most capable AI tools for solving very complex problems. Chatbots, AI-augmented legal researchers and big data analytics are examples of how AI has started to transform the legal landscape. If you have any questions on how best to use AI or other legal technologies within your team, contact LegalVision’s legal transformation lawyers on 1300 544 755 or fill out the form on this page. 


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