Lawyers can use AI in a variety of ways to improve their efficiency and productivity. AI can provide much-needed assistance in case count management, data collection, case finding, and more. In addition, AI can be used to better serve and advise customers. And yes, it can help streamline various processes, including your contracting process. Let`s take a closer look at some of the ways AI is changing the legal profession. One area where unsupervised machine learning has increased is the area where companies need to group or group customers by adopting a buying behavior. In a legal context, such as the review of legal documents, unattended machine learning aggregates similar documents or clauses as well as clear anomalies from these groups. This would therefore reduce the time required for manual review and allow more time to be spent examining outliers when clauses or documents were different. The first company to implement LTS technology was Atrium, which began as a way to combat the traditionally slow-to-adopt industry when it came to technology. Founded in collaboration with Kan, the firm aims to “make business access to legal services transparent and predictable in terms of price by building the largest structured data platform for organizational and contractual data. Model the best way to store this information and find new ways for lawyers and paralegals to interact with the resulting structured data to advise clients. “A handful of AI teams are creating machine learning models to predict the outcomes of pending cases, using as inputs the corpus of relevant precedents and the particular factual model of a case. However, there is also the due process problem of the lack of transparency and explainability in the use of AI.

You cannot cross-examine an artificial deep learning neural network. At least not yet! AI is a mirror of humanity and reveals some of our inherent flaws. The process of dealing with why an AI makes a recommendation can lead us to better understand the reality and limitations of human explanations or rationalizations of its decisions. Some AI solutions offer the ability to automate the review of legal research, allowing judges to review submissions and identify missing precedents and authorities. Similar tools are also available for lawyers. Similarly, AI can be used in the transactional space in document automation, leveraging large databases with precedents to support document creation and analysis. All these innovations show the growing potential of AI in the legal space. “We are already seeing significant benefits from sophisticated parties leveraging legal predictive technologies for machine learning,” said Benjamin Alarie, CEO of Blue J Legal. “Over the next decade, these algorithmic technologies will become the natural starting point for legal advice.” This could include, for example, data or certain clauses in a legal context. Alternatively, when you click Play on your favorite Netflix show, you tell their machine learning exactly what types of shows interest you, and the more shows you watch, the more accurate it becomes when you predict which shows you would be most interested in. But more importantly, the idea of allowing algorithms to make deprivation of liberty decisions may simply be unscrupulous. It`s not inconceivable that machine learning algorithms predict with great confidence when a person is likely to commit a future crime, like the sci-fi movie Minority Report.

Another compelling reason to limit the use of AI in a criminal context could be that judges, lawyers, and society as a whole may have too much confidence in these algorithms. Even when people retain ultimate decision-making power, it`s not uncommon for them to rely excessively on technology-based recommendations, a phenomenon called automation bias. With AI, this trust can be particularly misplaced, as the actual capabilities of the technology may not be as “smart” as they seem. Supervised learning The most popular archetype of machine learning is supervised learning. Most machine learning is based on supervised learning. Here, the data is labeled or classified to tell the machine exactly which models to look for. Artificial intelligence (AI) is just beginning to develop in terms of use by lawyers and in the legal industry. What is the impact of this technology on the legal profession? Over the next few years, we will be at the dawn of a revolution in legal practice, led by the introduction of artificial intelligence – especially by in-house lawyers. Just as email has changed the way we do business every day, AI will become ubiquitous – a must-have assistant for virtually every lawyer. Those who do not accept and embrace change are left behind. Those who do will ultimately feel free to do the two things for which there always seems to be too little time: reflection and advice. Traditionally considered a cost center and largely neglected by entrepreneurs, the legal function has seen little innovation in recent years.

Today, Microsoft Word and email continue to be the dominant digital tools that legal departments use to do their jobs. There is a huge opportunity to automate this process. Startups such as Lawgeex, Klarity, Clearlaw and LexCheck are currently working on this vision. These companies are developing AI systems that can automatically adapt to proposed contracts, fully analyze them using natural language processing (NLP) technology, and determine which parts of the contract are acceptable and which are problematic. Like many others, you may be wondering what AI products exist or along the way and how you use them. Welcome to the first part of a four-part series on artificial intelligence and its impact on the legal industry, particularly how in-house legal departments will be affected. Over the course of the series, I`ll discuss what AI is, how it can be used by legal departments, and what you, as an in-house lawyer, should do next in terms of AI. As AI and ML continue to transform the legal profession, it is important that lawyers embrace these changes and use them to their advantage. Whether lawyers are turning to AI and ML-based platforms to manage the number of cases, collect data, or provide clients with better service and advice, staying on top of new advances in AI and ML technology is key to success as a lawyer. The programmer has set the reward policy (or the rules of the game), but machine learning has no idea what the right result should be. The machine understands that the goal is to get the most rewards, and will therefore constantly try different ways to get the biggest reward – and therefore eventually find a solution to the problem.

Like using treats to train a dog, reinforcement learning is the tool used by Google`s AlphaGo program to beat human chess masters. In addition to learning comes the interface, or how do humans and machine interact? For years, the most common way to enter information or queries into a computer has been to press Enter and wait for the response. These types of searches were done on Boolean logic, i.e. keyword searches. This means that each search is linear and has nothing to do with past or future research. With AI, this changes when every search is part of the learning process and every search and answer (and correction, if necessary) makes the machine much better for the next task. And just like my “Star Trek” example, most people now want to interact by talking to the machine. This is called natural language processing (NLP) and we see it, for example, in the way we interact with Apple® Siri® and Amazon® Alexa via voice. We will discuss this in more detail in Parts II and III of the series. The reason for the huge increase in AI spending is simple: there are huge productivity gains and cost savings when people are freed from routine tasks that computers can handle, allowing people to focus on tasks that really add value, things that computers really can`t or only do well.