Artificial intelligence (AI) is not merely a field of technical innovation and academic study. It’s also becoming indispensable in practicing law.
AI in the legal industry is rife with controversy as well as promise. The fundamental need that AI fills is having access to volumes of legal data at once without the means to efficiently study it and render actionable insights with the available time and resources.
Artificial intelligence, like its applications in medicine and other industries, can make practicing law faster. AI can quickly sort through mass legal data, make sense of any patterns, and recommend the next steps to take.
AI in law today
When it comes to practicing law, data takes the form of historical and current case files, contracts, proposals, and more.
AI applications are set to disrupt the entirety of the legal services market, which is valued at $750 billion worldwide, according to Statista. Artificial intelligence could dramatically expand the value and usefulness of this field, while helping it better use the mass data available to its practitioners.
Study of this burgeoning technology suggests the legal AI market could grow more than 35% per year until 2026 to $37.8 billion, according to Zion Market Research. What are the implications of this AI growth for legal professionals?
5 ways AI is changing law
1. Legal discovery and document analysis
Document analysis is a major part of the discovery phase of litigation. Discovery is when the attorneys involved in a case collect and draw insights into the situation. During this stage, lawyers determine whether existing legal precedent is on their side and what kind of language or previous cases might sharpen their arguments.
ROSS Intelligence, Kira Systems, Casetext, and others all want their research platforms to be the go-to AI tool in this area. Analyzing documents is time-consuming work for attorneys, paralegals, and legal clerks. However, this task is ideal for AI and its natural language processing (NLP) abilities. Having AI pore over troves of documents and isolating relevant sections ensures no effort goes to waste and frees legal professionals to focus on the humanistic parts of their jobs.
2. Divorce proceedings and settlements
Nobody wants to think about the dissolution of their marriage, but divorce is a core area of civil law. The word itself is enough to cause couples to wince at the thought of months- or years-long proceedings where both parties argue over the details.
AI plays a role in helping couples navigate divorce in a straightforward, streamlined, and easy-to-understand way. This AI application involves a couple filling out a questionnaire and providing other details about their circumstances. AI products from companies like Amica, Penda, and Wevorce then call on databases of previous divorce cases to recommend amicable solutions.
Typical divorce cases chew up court time and are expensive for couples. Many cases might benefit from the expediency and impartiality offered by machine learning (ML)-driven resolution.
See more: Artificial Intelligence: Current and Future Trends
3. Fraud detection and prevention
Protecting against fraud is a top priority for organizations. For instance, it’s not uncommon for the U.S. federal government to recover more than $2 billion per year in fraud damages through whistleblowers. The global pandemic also resulted in an uptick in cyber attacks, including phishing, ransomware, and various types of fraud.
In PwC’s 2020 “Economic Crime and Fraud Survey,” 80% of fraud experts said they expect AI to reduce instances of financial fraud. Of the respondents, 64% expressed confidence that AI would eventually prevent fraud entirely.
Most banks complain about fraud investigations taking too long or delivering false-positives, which is why AI is a promising addition to this area of law. Detecting fraudulent activity often comes down to comparing events with past transactions, patterns, or records. AI can do this quickly.
Financial institutions holding $100 billion or more in assets commonly invest in such tools. However, a day is quickly approaching when banks and organizations of all sizes begin adopting AI-backed fraud platforms designed for fast responses and preventive measures.
4. Drafting and reviewing contracts
Business agreements, mergers, and other high-stakes business moves are usually outlined on paper. Drawing up contracts between individuals or businesses is time-consuming. It’s also not a process that doesn’t allow for errors or oversights.
Choosing the wrong language or entity classification, not providing proper documentation, and failing to properly protect intellectual property (IP) rank among the most expensive mistakes to make when drawing up a contract. AI helps here as well.
Companies like Lawgeex, LexCheck, Leverton, Clearlaw, and others are applying AI to contract creation and vetting. Human legal experts could take days or weeks to look over a dense contract for errors or improvements. These AI tools can do the same work in a fraction of the time.
5. Predicting outcomes
This application of AI in law is controversial.
Lawmakers in France set up the nation for a showdown in 2019 when they moved to ban “judicial analytics.” This technology uses machine learning to attempt to predict criminal behavior or judicial outcomes. Whereas French lawmakers worry over the implications of predicting suspects’ and judges’ behavior, their counterparts in China are facilitating the exchange of data in the form of millions of public domain legal texts to make such AI platforms even smarter.
Some intentions may be benign, such as using mass data to study how the detention of suspects before trials impacts their success during plea bargaining.
Others, like predicting the behavior of suspects and defendants, are already under fire for demonstrable bias. For example, one AI assigned higher risk scores to Black defendants than it did to whites.
Predictive analytics is full of the potential for both good and misuse. Moving forward, technologists and lawyers alike will need new kinds of thinking, development, and training to ensure these algorithms don’t reproduce human biases.