Application of AI and Machine Learning in Legal
John McCarthy, the computer scientist and “father of AI”, defined artificial intelligence as the science and engineering of making intelligent machines, especially intelligent computer programmes.
“He was very unhappy with a lot of AI today, which provides some very useful applications but focuses on machine learning”.
Daphne Koller, Professor, Stanford University
When AI is mentioned in relation to corporate legal applications, it is indeed usually “machine learning”. Machine learning is the process by which systems learn from outcomes and decisions, and improve with experience, without being directly programmed to take certain actions or reach specific conclusions. These machines analyse data and learn patterns without significant human intervention – normally requiring just a training dataset to get going.
Machine learning is often confused with rules-based automation; workflows that are based on pre-programmed “if this then that” algorithms and it is important that legal buyers appreciate the difference when looking to deploy AI within their departments. If the machine isn’t analysing and learning from the data itself but is using pre-programmed, non-evolving rules to automate processes and outcomes, then it’s not AI.
For legal teams, machine learning is usually implemented to improve efficiency and productivity, as machines can perform tasks faster than a human, freeing legal counsel up to do higher-value work. These applications include:
- Legal Research: reviewing, tagging and ranking documents that are relevant to a matter or eDiscovery, including highlighting questionable ones that need human review.
- Contract Review: Identifying and flagging clauses for review, searching for missing clauses, and redlining in bulk and at speed.
- Invoice Review: Coding, approving, rejecting or flagging line items and invoices (where rules-based automation isn’t an option.)
- Data Extraction: This can apply to invoices, contracts, documents; any requirement where a mass of non-structured data needs to be organised and classified.
- Litigation analytics: Analysing trial data to predict outcomes of litigation.
Getting the most from machine learning
The above use cases and benefits have the power to transform the legal profession. However, legal departments that are currently implementing an AI-powered legal solution may be disappointed by the true scope of these tools, especially if they are at the start of their digitalisation journey. Buyers are not seeing the promised benefits and are beginning to question the hype.
The very nature of machine learning is that it needs data to deduce the patterns that help it to evolve and learn. This data doesn’t just need to be abundant in volume, it needs to be complete, accurate, fair and free of bias. Improved accuracy vs a human is a benefit often touted, but this is only the case if the data from which the machine is learning is accurate in the first place. Poor or insufficient data will mean the machine simply does not have enough data to learn from and it will not fully deliver the anticipated outcomes and benefits.
Perhaps even more concerning however is that the machine will draw partial or incorrect conclusions from a deficient dataset and take the wrong action or reach the incorrect conclusion – thereby creating hidden risk. Ironic that productivity can in fact be negatively impacted by AI, if a human must go back over the work, identify issues and correct it. More severe though, is if these incorrect conclusions result in damaging actions for the business, even litigation. The reliability of your machine learning needs to be a factor when accounting for legal risk, and it’s important for legal teams to understand their role in feeding machine learning tools with quality data and training to avoid these issues; as the saying goes, “you get out what you put in”.
If this is sounding overly paranoid, perhaps some examples from other industries will help show why it’s critical to be careful when deploying AI Back in 2018, Amazon created a tool to review engineering CVs and flag the top ones for interview. The intention was to automate a time consuming process. To train the machine, they used the dataset of current Amazon engineering employees plus applications from the last 10 years, which happened to be predominantly men. The machine ‘learnt’ that ‘more male’ candidates were the best for the role. Amazon soon ditched the tool. Poor data was also at the heart of IBM Watson’s failure to diagnose and treat cancer patients accurately. The data used to train the machine was hypothetical, rather than real patient data, and frequently gave bad advice. These examples demonstrate not only the importance of complete data for machine learning, but the fact it is hard to predict unexpected consequences before they’ve happened.
The above examples demonstrate the importance of quality and unbiased data, even when the aims are kept simple and straightforward. AI is not intended to do complex legal work; it speeds up routine tasks, supports better decision making and, in some cases, takes actions based on those decisions. In fact, some of the best examples of AI deployment are where machine learning tools have been combined with rules-based systems to first identify and categorise data and then take defined steps based on that categorisation.
Machine learning and eBilling
Spend management is one such legal-specific application where rules-based automation and machine learning can be used together. For example, eBilling.Space from BusyLamp has the following AI functionality for clients and/or law firms that prefer not to use LEDES files:
- Data extraction: Relevant information can be pulled from PDF invoices, which relieves smaller law firms from the burden of generating complex invoice-files.
- Invoice Reviews: Some law firms struggle to code invoices in a way that clients can understand. eBilling.Space AI takes unstructured invoice data and auto-classifies every task to enable automated invoice review.
- Legal Analytics: Unstructured invoice and matter data can be analysed to enhance strategic decision making.
- Block Billing: English time narratives can be analysed so that block billing, a practice that usually contravenes billing guidelines, can be identified.
Is AI right for your legal deparment?
Using point solutions such as the eBilling example above allows legal departments to take advantage of machine learning benefits for gains in specific areas of legal operations. But machine learning is by no means critical to make efficiency and productivity gains; most BusyLamp eBilling.Space clients start small and aim big by first tacking the issues of collating knowledge, structuring and cleansing their data sets, and building automated workflows.
When you gather requirements for your next legal technology project, start by mapping out your current processes, roadblocks and desired outcomes before looking at any particular technology tool. As you evaluate software vendors, you will discover various solutions and workflows to your problem, which may or may not involve AI.
Remember, you should never use AI for AI’s sake – it is rarely the silver bullet. Almost every legal technology tool uses rules-based (non-AI) automation to relieve the legal team of admin and mundane, repetitive tasks; this will be a fantastic starting point for most teams setting out on their digital journey.
There is no doubt that machine learning is playing a huge role in improving the productivity of the legal profession and will allow in-house teams to take a more pivotal, strategic role in their businesses. But as a profession that is very familiar with risk mitigation, a degree of caution needs to be applied when looking to reach the machine learning “promised land”. Accurate, high quantities of data alongside careful selection of technology tools will significantly reduce your exposure to these risks and help you make a success of your team’s digital transformation.
Because AI is so dependent on the data it receives, the real transformational tipping point will not be in the use of these solutions within the legal function alone but in the enterprise-wide application of machine learning tools. Imagine the insights and outcomes that could be achieved by analysing documents and data from across an entire organisation, not just the legal function. This is only achievable with integrated legal and enterprise tech tools, and robust, extensive, consistent data.
The ‘power of AI’ and the ability for it to truly change the legal profession is beyond question. However, it is important to proceed with caution and lay the groundwork to ensure that your legal department sees the benefit of machine learning, rather than learning that it has been sucked in by the AI hype machine.