The original definition proffered in 1956 by the man who coined the term AI, John McCarthy, is: “the science and engineering of making intelligent machines.” A more elaborate definition characterizes AI as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.”
Artificial intelligence (AI) has the potential to fundamentally change the practice of law, from automating repetitive tasks, to dramatically accelerating time to insight, to amplifying the decisions of legal practitioners across vast data sets. Despite the potential benefits and the seeming endless hype surrounding legal AI, adoption of this new tech remains slower across the legal ecosystem compared to other industries.
Many misconceptions and fears are holding legal practitioners back from embracing the tech-enabled future. But they don’t have to. Not only do AI’s benefits outweigh the risks, but in many cases, the bigger risk is maintaining the status quo.
Artificial Intelligence is a Lawyer’s Best Friend
AI tools are the modern legal professional’s tethered exoskeleton system. They work in parallel with legal teams’ current experience and abilities to provide an unprecedented level of expertise and acumen.
AI technology fits into four categories: machine learning, natural language processing, computer vision, and robotics. We will focus on the first three, as legal professionals use them most frequently.
In the context of the practice of law, legal research and eDiscovery in particular, the deployment of AI has generally fallen into the category of machine learning. Machine learning is a subset of AI characterized by the use of algorithms and statistical methods to enable machines to improve or learn through experience. Broadly speaking, most legal AI tools today rely on algorithms trained with human input to identify similar or dissimilar categories of documents to reduce the amount of time it takes to surface key concepts or evidence.
For legal professionals, two types of machine learning matter: unsupervised and supervised.
- Unsupervised machine learning often is an exercise in having computers “tell me something I don’t know.” AI tools point computer algorithms at data. The algorithms organize that data based on patterns, similarities, and differences. The algorithms work on their own. They do not rely on people to train them. They are not subject to the same biases. But they can learn from their own past experience.
- Supervised machine learning is a way to “find more like this.” With supervised machine learning, users train the system.
Natural Language Processing
Natural language processing (NLP) refers to AI technology that analyzes and understands natural language. Legal teams point NLP technology to text, which originates from communications such as chats, emails, social media posts, Slack messages, and tweets. It comes from documents such as contracts, advertisements, instruction manuals, warning labels, even web pages. It even comes from structured databases. NLP works with that text, performing sentiment analysis, measuring emotional signals, and summarizing blocks of text. It normalizes names, and it detects topics.
Computer vision is a third form of artificial intelligence used in document review. Aimed at a single photo of a streetscape, for example, computer vision AI technology was able to identify and label over a dozen items. These tools can go farther. Some of them also are astute enough to identify which regions of an image represent which entity. Some image labeling models even recognize logos, ad, and other targeted types of images.
The AI Model for Legal Tech: How it All Comes Together
Machine learning and natural language processing can be combined to create reusable AI models. Legal teams can use models built by others. If they have appropriate resources at their disposal, they also can build and run a wide array of their own models.
Legal firms use models to achieve many objectives and fit various business models. They use these models to find potentially privileged content. They deploy models to locate information that tends to support or refute asserted causes of actions and defenses to those causes. They run models to bring to light legal issues before those issues become formal complaints, bring on investigations, or lead to lawsuits.
eDiscovery AI Models
eDiscovery leverages deep learning and AI systems to parse large volumes of data and extract insights more quickly. Technology can capitalize on a combination of supervised, unsupervised, and reinforcement learning to provide legal professionals with key evidence and insights in a fraction of the time:
- Social Network Analysis: automatically surfaces communication patterns like who is speaking to who and with what frequency.
- Concept Clustering: Automatically visualizes data analytics and key concepts to accelerate matter understanding and prioritization.
- Technology Assisted Review: the AI learns from each coding decision then amplifies it across the data set and surfaces similar legal documents to dramatically accelerate document review speeds.
- BERT (Bidirectional Encoder Representations from Transformers) powered AI Model Sharing: Industry-leading AI builds a library of shareable and reusable models to help pre-train the technology to surface the information you need for your matter.
- Sentiment and Context Analysis: Uncovers hidden connections between people, places, and things to give you better insights into your data and case.
Contract Analytics Technology
The use of AI can exist throughout contract lifecycle management including clause extraction and anomaly detection, contract review, and due diligence. There are even smart contracts that reside on blockchain with automatic execution based on certain parameters. From inception through execution and lifecycle management, AI is helping to automate repetitive tasks and create more consistency for organizations across a portfolio of contracts.
As clients have become more sophisticated, they are demanding more data-driven decision-making from their outside counsel. As a result, there is a big focus on leveraging machine learning or AI to gain aggregate insights across a full portfolio of cases. Case management software serves as a single repository that enables practitioners to manage, store, and track legal cases and records like contacts, intakes, documents, events, tasks, and more.
Well deployed matter management systems optimize case intake, provide actionable data-driven insights about resourcing and tasks and even AI powered invoice generation and approval.
Project management software using AI can take case management solutions and data driven insights to the next level by helping clients manage important deadlines, manage client cases and documents, bill clients, and accept payments relating to their legal needs. With the aim of helping law firms and legal departments manage the business of being a law firm, their functionality spans a multitude of areas including case and client records, bookkeeping, billing, schedules, appointments, and more. More sophisticated platforms are leveraging machine learning and AI to extract business intelligence around billing and CapEx.
The holy grail of knowledge management is the ability to extract key pieces of information from the minds of lawyers and case teams in a searchable format that is accessible firmwide independent of any individual legal practitioner. From preserving and sharing institutional knowledge to ensuring the right talent is tapped for a given matter, the potential value of this AI powered offering is material.
Some practitioners are using AI powered insights from past matters to inform settlement discussions or funding analysis on current cases. In this fascinating emerging area of legal technology, AI software generates predictive results that forecast case outcomes based on various data inputs from past matters. Various AI tools on the market aggregate court decisions at the state or federal level to make predictions of rulings based on specific fact patterns, judges, or districts.
The Net-Net of AI in Legal
Both savvy firms and startups are doubling down on integrating AI technology across their organizations to differentiate themselves and gain a competitive advantage over the competition. Practitioners that embrace these emerging technologies are able to dramatically accelerate their career trajectory. The first step in embracing the AI powered future of the legal sector is for practitioners to educate themselves beyond what they may have learned in law school. Starting the journey now is critical, as the difference between those embracing tech versus those who are not is clear and present. The massive competitive advantage goes to those who are looking to AI.