The increased interest in AI and the shrinking gap between paralegals, legal researchers, and litigators are slowly changing the legal profession.
AI research and consulting services will be used by professional services businesses and law firms to help them evaluate how AI could improve the workflows of their professionals in an era of AI disruption. This post will describe our findings, which show the numerous applications of AI in the legal industry and how IT companies are attempting to automate business procedures.
The legal applications of AI fall into the following categories.
- Assisting attorneys with research and due diligence.
- Analytics offers more information and "shortcuts," which can be used to deliver more details.
- Legal job automation of creative processes (including some writing).
We encourage readers to skip to the most relevant areas due to the breadth of our study. The conclusion of this paper will discuss the potential of AI in the legal industry as well as some of its drawbacks.
Current advances in AI have numerous uses in the legal profession. One of the most renowned UK experts on technology and legal concerns holds this opinion.
According to experts, technology such as artificial intelligence (AI) and other advancements "allow computers to perform many duties that many people used to feel required of human lawyers and that this is not plateauing." Experts believe it is happening at a rapid pace.
However, the Lawyer App Development will eventually heat up.
AI In Law: Current Applications
Our review of legal offerings and firms reveals that existing implementations of AI fall within these areas.
- Due Diligence: Lawyers use AI tools to find historical data. This division includes electronic discovery, contract analysis, and legal research.
- Prediction Technology: An AI program generates predictions that predict the outcome of litigation.
- Legal Analytics: Attorneys can spot trends and patterns using prior case law, win/loss ratios, and judges records.
Well then examine artificial intelligences most recent legal uses.
Due Diligence
Verifying data and assessing a clients legal condition are tasks that fall under the purview of lawyers. This is necessary to advise clients intelligently on their options and the best actions to take.
Extensive due diligence can increase shareholder returns over the long term but can also be tiresome and time-consuming.
Legal professionals must undertake a thorough investigation to produce significant outcomes. Spot checks can also be prone to errors and inaccuracy by lawyers.
Kira Systems: The former M&A attorney at Kira Systems founded the company. Junior attorneys frequently make due diligence blunders, according to Kira Systems.
Examples include:
- Working on the weekends or after hours.
- Need to finish the due diligence by the end of the week.
- Doing nothing when the deal structure has been completely altered.
She says many colleagues have bad feelings regarding manual due diligence. Humans, including lawyers, can grow weary and irritable.
This has terrible repercussions for the extensive due diligence involved in M&A.
Kira Systems states that its software can conduct a more accurate due diligence contract evaluation. It accomplishes this by looking for, showcasing, and extracting pertinent content.
Other team members can utilize the software to look for information and links from the source. The technology may be used up to 40% faster the first time and up to 90% faster for individuals with more experience, claims the business.
LEVERTON: For real estate transactions, LEVERTON, a German Institute for Artificial Intelligence division, uses AI to extract data, manage paperwork, and prepare leases.
This cloud-based technology can quickly and in 20 different languages read contracts.
To assist them with their due diligence on a potential acquisition, Colliers International consulted an IT firm.
The data was organized on a spreadsheet after being extracted from thousands of papers using LEVERSONs AI.
E-Brevia: Lawyers can become overwhelmed by the review of multiple contracts. Moreover, they could overlook crucial adjustments that subsequently cause legal issues.
The co-founders of eBrevia had the same issue when they were just junior associates. They founded a business with Columbia University intending to speed up document review.
eBrevia asserts that it extracts relevant textual data from legal documents and other documents using natural language processing and machine learning.
This assists lawyers in analysis, due diligence, and lease abstraction. A lawyer can specify the information to be extracted from the scanned documents by using the program to turn them into searchable text.
The software will compile the extracted documents in an Excel report that can be shared or downloaded in various forms.
According to eBrevia, its analysis of 50 papers may be completed in under a minute and is 10% quicker than manual inspection.
The business provides specialized services, such as training its software to cater to the demands of particular businesses with thousands of papers. In August, the program was introduced in 11 offices in North America, Europe, Asia, and Europe. Nevertheless, since the business has yet to publicize its findings, its unclear how much money this technological advancement will cost the law firm.
JPMorgan: JPMorgan is one of many companies that have tapped AI. They also have specialized legal IT equipment.
JP Morgan says its COIN (short for Contract Intelligence) tool can quickly extract 150 features from more than 12,000 contracts and commercial credit agreements.
The corporation claims this is the equivalent of 36,000 hours of legal labor completed by its attorneys and loan officers.
The bank found that 12,000 wholesale contracts were annually executed with glaring mistakes. COIN was developed.
ThoughtRiver: Another player in the AI sector is ThoughtRiver. It manages portfolio reviews, contracts, and investigations to enhance risk management.
The Fathom Contextual Interpretation Engine was developed with machine learning specialists from Cambridge University.
The tool was created, so the business claims to automatically summarize extensive contract reviews. The AI can be used to read clauses and extracts from content.
It is said that the system can automatically flag potentially dangerous contracts. The business provides a brief overview of its product in the three-minute video below. It also displays the user interface and fundamental functionality.
LawGeex: LawGeex states that its software validates contracts when they adhere to predetermined rules.
The AI will recommend revision and acceptance if the contracts dont adhere to the established requirements. The business explains how it accomplishes this by utilizing legal expertise from lawyers, statistical benchmarking, text analysis, machine learning, and these techniques.
The business asserts that its technology may cut costs by 90% and enable legal firms to approve contracts 80% more quickly.
Yet, there is no correlation between these figures and any case studies. Deloitte is listed as one of its clients.
Legal Robot: Legal Robot, an AI business with headquarters in San Francisco, which provides Contract Analytics, addresses the rapidly expanding market for contract review software.
The organization is currently creating the beta version. It asserts that using machine learning and artificial intelligence, its software can transform legal content into numerical form and identify problems.
The software is demonstrated in a video that shows how it works. It generates a legal model of thousands of documents.
With this data, the contract is graded on language difficulty, legalese, and enforceability factors. The software then recommends how to increase the contracts coherence, clarity, and compliance. It also assesses it in light of risk factors, best practices, and jurisdictional variations.
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Ross Intelligence: In-depth legal research is necessary for each lawsuit and every court case. The sheer number of links, cases to review, and details to keep track of can be overwhelming for lawyers with limited time.
Attorneys can ask queries and obtain information, such as suggestions for readings and case law, using the ROSS Intelligence softwares natural language search capabilities.
ROSS worked for a corporation established 100 years prior in the bankruptcy division. This appeared to be a break with convention.
The legal firms chairman revealed in an interview that they used ROSS to manage 27 gigabytes of data. According to a report, ROSS is the law firms operation: "After scanning through trillions upon trillions of documents, ROSS will promptly respond to your questions.
ROSS asserts that legal professionals can interrogate ROSS in plain English by asking, "What is the Freedom of Information Act?" Then the next day, the next day, and the next day.
ROSS is believed to get better with use, just like most machine learning systems in general.
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Casetext: On the other hand, Casetexts CARA asserts that identifying prior legal uses of opinions enables attorneys to anticipate opposing counsels arguments.
Also, you might mark unfavorable treatment as evidence that some lawyers would not regard as trustworthy. Casetext claims that its clientele is prominent legal firms like Ogletree Deakins and DLA Piper.
Other Applications: However, only a few software tools combine legal analysis with machine learning.
Consider the following:
Loom provides win/loss percentages, judgment ruling details, and other helpful data. Yet, it is only accessible in a few Canadian provinces for civil matters.
A California state law service is called Judicata. Its program, Clerk, can read and analyze legal papers. After weighing the advantages and disadvantages of each brief, it offers a score based on the situation, the arguments, and the writing.
The user will find suggestions to fix content issues within the action items. The Clerk helps to reduce these errors by recognizing quotations in brief and cross-checking them against the cited cases to guarantee the language and page numbers are accurate, according to the product manager at the company.
Potential Bias Concerns: The University of Colorado Law School recently looked into whether searching internet legal case databases would yield the same findings.
The search algorithms employed by Casetext, Fastcase, and Lexis Advance for case databases, including Lexis Advance and Ravel, were shown to have biases about which case users would see.
Fastcase and Google Scholar are two more recent databases that have produced less relevant search results than earlier databases like Westlaw or Lexis.
As lawyers only require the most pertinent instances, according to Mart, search algorithms should produce redundant results from any legal web resource employed. When constructing algorithms, these engineers have preconceptions and biases. To identify the best cases, users should search several databases.
Using AI tools for legal reasons can be avoided because of one or a dozen papers. Therefore, its crucial to balance their advantages and disadvantages properly.
Machine learning systems are continuously influenced by the data they learn from; therefore, bias is not exclusive to the legal industry.
Everlaw: More companies are now producing discovery products that incorporate AI and machine intelligence.
Using at least 300 documents the user has deemed relevant or irrelevant, Everlaws predictive coding capability enables it to create prediction models.
AI analyzes metadata and contents to help classify documents. According to the company, users can easily identify the most relevant documents using the predictions model.
The software also suggests actions users can take to improve its predictive accuracy.
DISCO: With the help of its cloud technology, DISCO claims to get quicker results while looking for documents among massive amounts of data.
Like Everlaw, it also predicts which documents will be most pertinent to the user.
The AI gives tags scores (on a scale from -100 to +1100) to increase the precision of its predictions. The software displays search results and a score for each document, indicating which information could be most helpful to the reader.
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Catalyst: With its Automatic Redaction solution, Catalyst, a Denver-based business, helps lawyers and legal reviewers obliterate sensitive and confidential information from documents.
The company claims that manual redaction is laborious since it takes time for a reviewer to find specific text in a digital document and then add black boxes.
With the program, users can redact different sets of information from a single document after converting it to a digital format.
To find the proper word or phrase, users can conduct a word or phrase search. The software can also set redaction patterns, including social security numbers. The Automatic Redaction function of e-Discovery is described in this overview.
Exterro: Exterro asserts that it blends e-Discovery capabilities with the features of a project management system.
Users can conduct the legal study using the software, then work with others. Exterros legal clients assert that after implementing the system, they cut the number of extra workers from 100 to 5.
According to research, the program allowed lawyers to save 95% on the cost of performing eDiscovery duties. It lists Microsoft, AOL, and Target as among its most notable clients.
Brainspace Discovery: According to the users document search, Brainspace Discovery groups and arranges documents.
The AI searches for documents using concept search. This search looks for documents with similar terms but not necessarily words and phrases. Additionally, it uses term or phrase extensions to tell the computer to remove terms from the results that were inadvertently connected.
To refine the search, categorization is employed in the end. Further information on document digitization can be found in our article on how it applies to finance. By combining all three of these capabilities, this program may provide search results more pertinent to the users requirements.
Another AI-powered tool for contract assessment that offers legal professionals due diligence is:
- The M&A Due Diligence Robot from iManage is designed specifically for M&A papers. The review procedure is automated, and data from cluster sets are extracted.
- Computational linguistics technology is used by LitIQ to lessen legal conflicts involving contracts. The founder of LitIQ offers his opinions on the connection between law and machine learning.
- By utilizing its AI software to look for certain concepts within papers, like confidentiality agreements and general terms and conditions, LegalSifter believes it can save time and money.
- Seal, whose software was used by Dropbox and PayPal, and Experian were able to shorten the 255-day project completion time for a utility business by scanning through millions of contracts for those that contained particular conditions based on their case study.
- As the only tool that can search and rate uncommon and abnormal documents and clauses for lawyers, Luminance makes this claim.
Despite increased usage, California currently needs help with the e-program. Discoverys The State Bar has made a new Proposed Formal Opinion available.
Lawyers must have a fundamental understanding of how the eDiscovery system works. They will receive punishment if they dont. The State Bar advises lawyers not adept at using the facility to either learn how to do so or employ someone else.
Or decline to be represented.
Prediction Technology
Professors from Washington University examined the precision of their algorithm for predicting Supreme Court rulings in all 628 contested cases.
The outcomes of a team of specialists were contrasted with those of the algorithm. Compared to the experts accuracy of 59 percent, the statistical model developed by the researchers was able to anticipate events with an accuracy of 75%.
There are numerous other applications for predictive analytics, even though every sector has its challenges. Take a look at our article on Predictive Analytics In Banking. To address problems in the actual world, this article discusses how AI systems employ predictive analytics.
Read More: Why You Need a Lawyer When Developing An App 2023
Two of his coworkers and a professor from Michigan State University successfully extended the scope of Supreme Court rulings from 1816 to 2015.
A professor at University College London and his colleagues who utilized machine learning to examine the European Court of Human Rights case text reported similar outcomes. They correctly predicted the result 79 percent of the time. In some practice areas, quantitative legal prediction is significant, according to a professors paper.
This role will likely increase as more legal data is available.
Intraspexion: Many AI businesses have joined this market, including Intraspexion, which offers patent software systems that may notify attorneys of potential legal concerns.
The system looks for high-risk papers and presents them based on the risk level determined by the AI. When a user clicks on the document, the system highlights danger terms that have been recognized by subject matter experts.
The company claims that using the software allows users to choose which papers could put them in danger of litigation. Contact us for Lawyer Application Development if you also want an intraspexion like app.
Ravel Law: Another tool, Ravel Law, can predict the result based on pertinent precedent, court decisions, and quotations from more than 400 courts.
Lawyers can view specific judges cases, citations, circuits, and rulings using the products Judge Dashboard capabilities. This is meant to assist them in determining how likely it is that a court will issue a particular case.
The CEO of the firm confirms this claim in an interview. Ravel Law, he said, may support litigation strategy by offering data on the judgment-making processes of judges.
Lex Machina: Several capabilities are available on the Lex Machina Legal Analytics Platform to help attorneys with their legal strategy.
For instance, the Timing Analytics tool predicts when a case will go to trial before a specific judge using AI. Users of the Party Group Editor can select attorneys and examine, among other things, their expertise appearing before judges and courts and the number of litigation cases in which they have been involved.
Premonition: The largest litigation database in the world, according to Premonition. It predicts a lawyers success by pairing him with a judge by looking at his victory record, case length, and the type and pairing him with a judge.
The accuracy for a typical case result is 30.7%. According to the manufacturer, the gadget can aid in examining various cases and estimating how long each one will last.
AI tools for predictive technology require much more data than any other analytics platform. For instance, case files are necessary to ensure the tool can completely work according to business needs.
This article describes the model as "exceptionally intricate." This is because predicting a judges vote requires over 95 variables with accurate values up to 4 decimal places and almost 4,000 random decision trees. Except for a few that demand access fees, the corporation concedes that no database can properly support the product.
Legal Analytics
Lawyers can use docket entries and case documents to gain additional insight during litigation. Modern AI techniques assert that they can extract crucial information from these documents to bolster claims.
Lex Machina: A litigator who uses Lex Machinas Legal Analytics software to identify "the Plaintiff, the Plaintiffs Counsel, the Parties they have Represented, and the Parties they have Sued." Data generated by the software can be used to analyze the chances of opposing counsel succeeding or failing in a case.
The program produces data that can be used to build a story. Your email to research, get the first findings, then iterate the procedure. This normally takes at least a day.
When defending a generic pharmaceutical manufacturer, an intellectual property lawyer employed the program. She could see the judges prior decisions and how they favored instances just like hers, thanks to the Legal Analytics application.
Experts said that both parties reached a settlement, which resulted in a more satisfactory resolution.
Ravel Law: A law practice can use data to present itself to prospective clients. It provides intelligence on opposing counsel and generates values on case probability.
The dashboard of lawyers using Ravel Laws software is said to contain information from judges on cases, circuits, and decisions. To attract new clients, use this information. The business is now collaborating with Harvard Law School to digitize the facultys collection of American case law.
The tech platform will make this available.
Settlement Analytics: It is a good idea to have a backup plan. The CEO of Settlement Analytics outlined how this technology can generate results with poor accuracy in his article.
This leads to tiny sample sizes after filtering, cognitive biases, and the propensity to interpret random patterns as legitimate. There is a lot of data noise as well.
According to experts, quantitative legal data analysis "may be more challenging and error-prone than it is often acknowledged." Data analytics can be thought of as a simple tool.
It is simple to ignore the science that underlies what is essentially data science, though. Making decisions may be negatively impacted by this.
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Conclusion: Thoughts On Ai And Legal Practice
The examination of dozens and dozens of AI-tech businesses and use cases has brought up various issues about adoption.
Which businesses will use these technologies, you ask? And which legal-tech AI applications are most likely to spread quickly in the next few years? As we wrap up this piece, well discuss a potential catch-22 for AI in law or the legal profession.
The best-known use of AI in the legal industry is to increase productivity. Algorithms are used by AI software to speed up document processing and find mistakes.
This is paradoxical because lawyers frequently take longer to accomplish jobs or papers than others, and the legal industry has historically relied on "billable hour" billing. It is unlikely that fewer manual jobs will encourage the use of AI (or boring ones).
Peer pressure will be the main reason that AI adoption is pushed. While they are more likely to be able to move swiftly and pass the savings on to clients, peer pressure will probably lead legal firms to use AI.
Businesses that cannot automate risk having to pay a high price for legal services that other businesses have mostly automated.
How the shift from legal AI will take place is still being determined. According to one theory, large law firms may be the first to utilize AI-based tools and integrations since they have the greatest funding.
Because they dont have the overhead of larger companies, newer businesses will probably choose an approach that is more automated, and efficiency-driven, to begin with. If you are a law firm and want your own app contact us to get app designer for hire.
