LawTechnos | Legal Technology Magazine

Computer Scientist John McCarthy, more popularly known as “The  father of AI”, defined Artificial Intelligence as the science and  engineering of making intelligent machines, specifically intelligent  computer programmes. When AI is mentioned with reference to corporate legal applications, it  is usually regarded as “Machine Learning”. Machine Learning is the  process by which legal software learns from outcomes and decisions,  and keeps improving with more inputs and 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 valid training  dataset to get going.

Quite often, Machine Learning is 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 corporate 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,  billable 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. 

MACHINE LEARNING AND EBILLING

Spend management is one such legal-specific application where rules based automation and machine learning can be used together. For  instance, Smart Legal Counsel from Beveron has the following  functionality for corporate legal departments:

• 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. Smart Legal Counsel 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. 

TO SUMMARIZE…

Remember, you should never use any software tool / AI just for its 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. The ‘power of LegalTech’ 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.