AI News, AI in Law and Legal Practice artificial intelligence
Artificial intelligence (AI) and law librarianship
CHICAGO — Futurists predict that in the next ten years the profession of “lawyer” will splinter into job titles like “legal process analyst” or “legal knowledge engineer.” And some in the field are already taking a proactive approach — in fact, more than two dozen law schools have developed innovation centers to explore artificial intelligence (AI) and the law.
Edited by Ellyssa Kroski, in this collection readers will learn how AI technology is changing law school curricula, lawyer practice, marketing, and other key aspects of the field.
ALA Editions and ALA Neal-Schuman publishes resources used worldwide by tens of thousands of library and information professionals to improve programs, build on best practices, develop leadership, and for personal professional development.
Artificial Intelligence: Educating the Legal Profession
Intelligence is already appearing in our courts and law offices. Like any tool, automated decision systems require a degree of competence to be used responsibly. As the legal profession increasingly interacts with and relies on artificial intelligence, it becomes increasingly important that members of the profession understand it.
At this conference, we hope to unveil two different ways to provide an introductory level of education on some of the key issues, one focusing on technical competence and statistical literacy and the other focusing on key ethical issues. Last, we will host a discussion dedicated to the important issue of ensuring that our judiciary understands the automated decisions systems that are already appearing in both civil and criminal cases.
the Judiciary How are judges currently educated on AI issues? How can we ensure that, going forward, judges are competent to understand automated decision systems, ensure they are deployed appropriately during litigation, and monitor parties' use of these technologies in their cases.
AI in Law and Legal Practice – A Comprehensive View of 35 Current Applications
Artificial intelligence (AI) companies continue to find ways of developing technology that will manage laborious tasks in different industries for better speed and accuracy.
We break down AI’s current legal applications into the following categories of applications: Because of the breadth of our research (and hence the length of this article), we encourage readers to feel free to skip ahead to the applications areas of greatest interest for them.
Based on our assessment of the companies and offerings in the legal field, current applications of AI appear to fall in six major categories: Next, we’ll explore the major areas of current AI applications in law, individually and in depth: (We’ve done our best to place companies into the category that best represents their product offering, but it’s important to note that there is overlap on many of the groupings we’ve chosen.) One of the primary tasks that lawyers perform on behalf of their clients the confirmation of facts and figures, and thoroughly assessing a legal situation.
These include working very late at night or on the eve of a weekend, forgetting to perform due diligence before the end of the work week, and failing to act on it when a deal structure is completely revised.
Lawyers, being human, get tired and cranky, with unfortunate implications for voluminous due diligence in M&A.” Kira Systems asserts that its software is capable of performing a more accurate due diligence contract review by searching, highlighting, and extracting relevant content for analysis.
eBrevia claims to use natural language processing and machine learning to extract relevant textual data from legal contracts and other documents to guide lawyers in analysis, due diligence and lease abstraction.
The company provides a brief tour of their product in the 3-minute video below, including a detailed look at the user interface and basic functions of the software: LawGeex claims that its software validates contracts if they are within predefined policies.
The company also claims that with their tool, law firms can cut costs by 90 percent and reduce contract review and approval time by 80 percent (though these numbers don’t seem to be coupled with any case studies).
With the issues flagged by the software, it then provides suggestions on improving the contract’s compliance, consistency, and readability by evaluating it on best practices, risk factors and differences in jurisdiction.
Lawyers can take advantage of the natural language search capability of the ROSS Intelligence software by asking questions, and receiving information such as recommended readings, related case law and secondary resources.
In an interview with Stanford Law School, Mona Datt (co-founder of Loom Analytics) elaborates: “Instead of performing open text searches looking for personal injury precedents, a lawyer could use Loom’s system to see all personal injury decisions that were published in a given time span and then break them down by outcome.
Instead of combing through individual decisions looking for ones written by a particular judge, Loom’s system can show all decisions authored by that particular judge and provide an at-a-glance snapshot of their ruling history.
In the company’s blog, Product Manager Beth Hoover explains, “Clerk helps reduce these errors by identifying the quotations in a brief and cross checking them against the cited case to ensure the text is identical and the page numbers are correct.” In a recent paper by Susan Nevelow Mart of the University of Colorado Law School tested if online legal case databases would return the same relevant search results.
There has been a growth in the number of e-discovery product manufacturers that harness AI and machine learning. Everlaw uses its predictive coding feature to create prediction models based on at least 300 documents that were classified before as relevant or irrelevant by the user.
You chan check a demonstration of Everlaw’s Prediction Technology feature in this video: DISCO claims to deliver faster results using its cloud technology for document search on large data volumes.
In the promotional video below, Dr. Alan Lockett (DISCO’s Head of Data Science) explains the company’s technology in simple terms: Denver-based Catalyst markets its Automated Redaction product to help lawyers and legal reviewers remove sensitive and confidential information on documents.
“Manual redaction”, as the company claims, is cumbersome considering the amount of time that a reviewer spends on locating content on a digital document and then applying black boxes on these statements.
Their tool allows users to convert a document to digital format and then perform multiple sets of redactions for a single document by searching for a word or phrase.
When finding documents, the AI employs concept search (searching for documents that are similar in concept but not necessarily in words or phrases), term or phrase extension (instructing the software to remove terms incorrectly associated with the results), and classification (specifying another category to refine the search).
Other AI-powered contract review platforms that cater to due diligence for legal professionals include: While there has been a growth in the use of e-Discovery tools, its application has become a public issue in states such as California.
Daniel Kantz, in his 2012 paper, stated, “Quantitative legal prediction already plays a significant role in certain practice areas and this role is likely to increase as greater access to appropriate legal data becomes available.” Indeed, several AI companies have ventured into this field such as Intraspexion, which has patented software systems that claim to present early warning signs to lawyers when the AI tool detects threats of litigation.
In the video below, the product’s user interface is featured and sample analytics results are presented: Finally, Premonition, which claims to be the world’s largest litigation database, asserts to have invented the concept of predicting a lawyer’s success by analyzing his win rate, case duration and type, and his pairing with a judge at an accuracy of 30.7 percent average case outcome.
In an article, the model is described as “exceptionally complicated.” That’s because it needs almost 95 variables (with almost precise values up to four decimal places) supported by almost 4,000 randomized decision trees to predict a judge’s vote.
Hogan Lovells litigation attorney Dr. Chris Mammen, uses Lex Machina’s Legal Analytics software to find out “who is the plaintiff, who is their counsel, who have they represented, and who else have they sued.” The software generates data that can be used to analyze an opposing counsel’s likelihood of winning or losing a case.
The data can also be used in pitching a law firm’s services to potential clients by providing intelligence on the opposing counsel, generating values on probability of winning the case and identifying litigation trends to use in their marketing campaigns.
discover real world banking-sector examples of the concepts outlined in this article. Neota Logic System claims that its software PerfectNDA shortens the nondisclosure agreement (NDA) process by offering templates selected by AI according to a user’s scenario.
if as a business owner you launched a product within the last year, you need to talk to a patent attorney right away to make sure it is protected.” TrademarkNow is a company taking on some of the manual knowledge work of intellectual property application with AI.
It uses a complex algorithm that is said to shorten weeklong searches for patents, registered products and trademark using the Trademark Clearance platform, which returns search results in less than 15 seconds according to the company’s claims.
The company’s data sheet states that it’s the first patent application-drafting tool for lawyers that save four hours on provisional patent application and 20 hours and non-provisional types.
In a case study listed on TurboPatent’s website, two paralegals from the Pacific Patent Group used the software to perform document retrieval, bibliographic data research, examiner remarks review and rejection issues discovery.
TurboPatent claims that Pacific’s paralegals were 500-800% more productive in their tasks when using SmartShell (thought the case study isn’t clear what exact tasks were relevant for the software, and which weren’t –
brief overview of how the product’s functions and value proposition can be seen in the 1-minute video below: Electronic Billing platforms provide an alternative to paper-based invoicing with the goal of reducing disputes on line items, more accurate client adjustments, (potentially) more accurate reporting and tracking, and reduced paper costs.
The company claims that its average client reduces administrative costs related to payment management by 8 to 12 percent by using the platform’s assisted review feature.
For example, some corporate clients refuse to pay for more than ten hours of a single lawyer’s time billed in one day unless the lawyer is physically at a trial site.” Echoing Herrman’s sentiment, Wayne Nykyforchyn, CEO of InvoicePrep, states that e-billing falls short on making judgmentson its own results.
Legal firms who adopt AI and are able to move faster may be more likely to pass those savings immediately on to their clients, and firms with no ability to automate may find themselves relatively overpriced for legal services that other firms have largely automated away.
These firms will likely apply AI and other software to a specific legal domain (possible wills and trusts, or patent law, or commercial real estate contract review, etc), and they’ll be able to leverage technology to garner large profit-per-employee numbers.
For example, a narrow new tech-driven legal firm focused on patent law might streamline their lead generation and sales process, and automate a huge majority (or at least a significant portion) of their services with natural language processing and AI.
Some lawyers will argue that ‘Smart people won’t like this technology, they’ll want it done the old fashioned way.’ Indeed for some legal matters there may be little choice but to leverage human expertise, but other processes and services will be augmented heavily by AI, and the field itself will eventually have to shift.