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AI in the Legal Industry but Can't Overtake the Human Aspect of the Law.

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Artificial intelligence (AI) companies continue to find ways of developing technology that will manage laborious tasks in different industries for better speed and accuracy . In the legal profession, AI has already found its way into supporting lawyers and clients alike.

The growing interest in applying AI in law is said to be slowly transforming the profession and closing in on the work of paralegals, legal researchers, and litigators.

In this article, we'll discuss the different ways in which AI is currently applied in the legal profession and how technology providers are trying to streamline work processes. We break down AI's current legal applications into the following categories of applications :

Helping lawyers perform due diligence and research

Providing additional insights and "shortcuts" through analytics

Automating creative processes (including some writing) in legal work

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.

We'll conclude this article with some thoughts on AI's promise and limitations across the legal industry.

Current developments in AI in legal practice come in various applications. Richard Susskind, one of the UK's most respected thinkers at the intersection of legal and technology, believes that this trend will continue to grow in the coming years.

AI in Law: Current Applications - Insights Up Front

Based on our assessment of the companies and offerings in the legal field, current applications of AI appear to fall in six major categories:

Due diligence - Litigators perform due diligence with the help of AI tools to uncover background information. We've decided to include contract review, legal research and electronic discovery in this section.

Prediction technology - An AI software generates results that forecast litigation outcome.

Legal analytics - Lawyers can use data points from past case law, win/loss rates and a judge's history to be used for trends and patterns.

Document automation - Law firms use software templates to create filled out documents based on data input.

Intellectual property - AI tools guide lawyers in analyzing large IP portfolios and drawing insights from the content.

Electronic billing - Lawyers' billable hours are computed automatically.

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.)
Due Diligence

One of the primary tasks that lawyers perform o n behalf of their clients the confirmation of facts and figures, and thoroughly assessing a legal situation. This due diligence process is required for intelligently advising clients on what their options are, and what actions they should take.

W hile extensive due diligence can positively impact long-term shareholder returns (according to a by the City University of London), the process can also be very time-consuming and tedious. Lawyers need to conduct a comprehensive investigation for meaningful results. As such, lawyers are also prone to mistakes and inaccuracy when doing spot checks.

Kira Systems

Noah Waisberg , a former M&A lawyer who founded the software company Kira Systems, thinks that due diligence errors by junior lawyers often occur for a number of reasons. 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.

He adds, "Many associates are in a certain negative mood about the efficacy of manual due diligence. 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. Other team members who need to perform multiple reviews of the content can search for the extracted information with links to the original source using the software. The company claims that its system can complete the task up to 40 percent faster when using it for the first time, and up to 90 percent for those with more experience.

LEVERTON

, an offshoot of the German Institute for Artificial Intelligence, also uses AI to extract relevant data, manage documents and compile leases in real estate transactions. The cloud-based tool is said to be capable of reading contracts at high speeds in 20 languages.

In 2015, IT firm Atos sought the help of real estate firm Colliers International, which used LEVERTON in performing due diligence of a company that the former was about to acquire. Through the use of LEVERSON's AI, information such as payable rent, maintenance costs and expiration dates were extracted from thousands of documents and then organized on a spreadsheet.

eBrevia

However, lawyers can be burdened by reviewing multiple contracts and they may miss important edits that result to legal issues later on. This is the same problem that Ned Gannon and Adam Nguyen, co-founders of , experienced when they were still working as junior associates. They built a startup in partnership with Columbia University with the intention of shortening the document review process .

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. A lawyer would have to customize the type of information that need to be extracted from scanned documents, and the software will then convert it to searchable text. The software will summarize the extracted documents into a report that can be shared and downloaded in different formats.

Here's a quick introduction on how eBrevia captures content and displays relevant information to users.

On its , eBrevia claims that it can analyze more than 50 documents in less than a minute, 10 percent more accurate than a manual review process. The company also offers bespoke solutions by training its software to customize specific requirements of firms that require thousands of documents for rapid review. Baker McKenzie rolled out the software in 11 offices in Asia, Europe and North America in August 2017. The financial impact of this tech innovation in the law firm, however, is still unknown as the company has yet to release its findings.

JPMorgan

Other organizations such as JPMorgan in June 2016 have tapped AI by developing in-house legal technology tools. JP Morgan claims that their program, named COIN (short for Contract Intelligence), extracts 150 attributes from 12,000 commercial credit agreements and contracts in only a few seconds.

ThoughtRiver

Other AI industry players include ThoughtRiver , which handles contracts, portfolio reviews and investigations for improved risk management. Its Fathom Contextual Interpretation Engine was developed together with machine learning experts authorities at Cambridge University.

The company states that it designed the product to automate summaries of high-volume contract reviews. While users users read content extracts, they can also read the meanings of clauses provided by the AI. The system is also said to be capable of flagging risky contracts automatically. 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. If they fail to meet the standards, then the AI provides suggestions for editing and approval. It does this by combining machine learning, text analytics, statistical benchmarks and legal knowledge by lawyers according to the company.

In this video, LawGeex CEO Noory Bechor further explains how his product can cater to legal services.

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). The firm lists Deloitte and Sears among some of its current customers.

Legal Robot

Ross Intelligence

Every lawsuit and court case requires diligent legal research. However, the amount of links to open, cases to read and information to note, can overwhelm lawyers who have limited time doing research. 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.

BakerHostetler, in what seemed like a break in tradition, employed ROSS in its bankruptcy department, 100 years after the law firm's founding. The law firm's chairman, Steven Kestner, explained in an that they decided to employ the software to work on 27 terabytes of data. A Forbes report describes ROSS' function in the law firm's operations: "ROSS will be able to quickly respond to questions after searching through billions of documents.

The company claims that lawyers can ask ROSS questions in plain English such as "what is the Freedom of Information Act?" and the software will respond with references and citations. Like most machine learning systems, ROSS purportedly improves with use.

Casetext

Casetext claims large law firms such as DLA Piper and Ogletree Deakins as its clients.

Other Assorted Applications

Other software products also combine machine learning and legal analysis to assist lawyers with legal research but with limited coverage. For example:

features win/loss rates and judge ruling information but only for civil cases in select Canadian provinces. In an 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 short, we're providing quantitative metrics on Canadian case law."

, on the other hand, only serves California state law as of this writing. Its software, Clerk, is said to be capable of reading and analyzing legal briefs. It also evaluates their pros and cons and then assigns a score for each brief based on arguments, drafting and context.

The recommendations intended to reduce content mistakes are listed as part of the action items for the user. In the company's , 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."

Potential Bias Concerns

For example, newer databases such as Fastcase and Google Scholar have generated less relevant search results compared to older databases such as Westlaw and Lexis. Mart argues that search algorithms should be able to generate redundant results on whatever legal online database is used since lawyers need only the most relevant cases. However, because these engineers have biases and assumptions when developing their algorithms, users are recommended to use multiple databases in order to find out the cases that fit their needs.

By no means does a single paper (or a dozen papers) imply that legal AI tools shouldn't be used, but rather that their pros and cons are yet to be weighed out thoroughly. Bias is by no means specific to the legal field, as machine learning systems are always influenced by the data that they're trained on.

Readers with an interest in AI bias and the ethical considerations of discrimination by machines may benefit from reading our recent article in collaboration with the IEEE: Should Business Leaders Care About AI Ethics?

Everlaw

The AI looks into the contents and metadata and uses such information to classify other documents. The company claims that the prediction model's results can help users easily identify which documents are most relevant. It also recommends actions on the part of the user on how to improve the software's predictive accuracy of the model.

You chan check a demonstration of Everlaw's Prediction Technology feature in this video:

DISCO

DISCO claims to deliver faster results using its cloud technology for document search on large data volumes. Similar to Everlaw, it also employs prediction technology to suggest which documents are most likely to be relevant or irrelevant to the user.

The AI works by assigning scores on tags (on a scale of -100 to +100) in order to improve its prediction results. The software displays its search results with each document's score and suggests which material is most likely useful for the reader. In the promotional video below, Dr. Alan Lockett (DISCO's Head of Data Science) explains the company's technology in simple terms:

Catalyst

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.

Exterro

Brainspace Discovery

to match closely to a user's document search. 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). The topic of Brainspace Discovery clusters and sorts documents document digitization and search is explored further in our article on Document Digitization in Finance. The company claims that by combining these three features, the software can better deliver document search results closer to a user's needs.

Other AI-powered contract review platforms that cater to due diligence for legal professionals include:

iManage's RAVN whose M&A Due Diligence Robot is designed for M&A documents to automate the review process and extract data from cluster sets;

LitIQ, which capitalizes on computational linguistics technology to reduce contract-related disputes (Gary Sangha, founder of LitIQ shares his thoughts on the relationship of machine learning and law in this interview);

LegalSifter, which claims to cut time and financial costs through its AI software that looks for specific concepts in documents such as general terms and conditions and confidentiality agreements;

Seal, whose software is used by Dropbox, PayPal and Experian, was able to reduce the time spent to 48 hours from 255 days by a utility company by searching for thousands of contracts with specific clauses according to their case study; and

Luminance, which claims to be the only tool that searches and ranks unusual and anomalous documents and clauses for lawyers.

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. In 2015, the State Bar released an amended Proposed Formal Opinion, requiring lawyers to have a decent knowledge on the to use the e-Discovery system or they will warrant discipline after being proven to have committed intentional or reckless acts. The State Bar also suggests that if a lawyer is incompetent on the facility, he should learn the skill, hire someone who's knowledgeable, or just simply decline representation.

Prediction Technology

In 2004, a group of professors from Washington University tested their algorithm's accuracy in forecasting Supreme Court decisions on all 628 argued cases in 2002. They compared their algorithm's results against a team of experts' findings. The statistical model by the researchers proved to be a better predictor by correctly forecasting 75 percent of the outcomes compared to the expert's 59 percent accuracy. Although in a separate industry with it's own separate problems numerous additional use-cases of predictive analytics can be found in our article on Predictive Analytics in Banking. This article illuminates AI programs utilizing predictive analytics to solve real world issues.

Expanding the coverage from 1816 to 2015, Prof. Daniel Katz of Michigan State University and his two colleagues achieved a 70.2 percent accuracy on case outcomes of the Supreme Court in their 2017 study. Similarly, Nikolaos Aletras of University College London and his team used machine learning to analyze case text of the European Court of Human Rights and reported a 79 percent accuracy on their outcome prediction.

Prof. 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."

Intraspexion

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.

The system works by searching for high-risk documents and displays them according to the level of risk that the AI has determined. When a user clicks on a document, risk terms as identified by subject matter experts through the algorithm are highlighted. According to the company, users can which documents put them at risk for litigation when they use the software.

Ravel Law

Another tool, Ravel Law, is said to be able to identify outcomes based on relevant case law, judge rulings and referenced language from more than 400 courts. The product's Judge Dashboard feature contains cases, citations, circuits and decisions of a specific judge that is said to aid lawyers in understanding how judge is likely to rule on a case.

The firm's CEO, Daniel Lewis, affirms such claim in this interview when he explained that the Ravel Law can aid in litigation strategy by providing information on how judges make decisions.

Lex Machina

Lex Machina's Legal Analytics Platform has a variety of features that are said to assist lawyers in their legal strategy. For example, the Timing Analytics feature uses AI to predict an estimated time when a case goes to trial before a specific judge.

The Party Group Editor, on the other hand, allows users to select lawyers and analyze their experience before a judge or a court and the number of lawsuits they were involved in before, among others. In the video below, the product's user interface is featured and sample analytics results are presented:

Premonition

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. According to the company, the product can also aid in looking at different cases and how long they're going to take for each attorney.

But similar to any analytics platform, AI tools that deal with predictive technology need a lot of data in the form of case documents to fully work according to Kantz. 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. Kratz admits that a database that will fully support his product is still not readily available except for a few ones that charge access fees to obtain data.

Legal Analytics

Case documents and docket entries provide supplementary insights during litigation by lawyers. Current AI tools claim that today's software products are able to extract key data points from these documents to support arguments.

Lex Machina

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. He claims to save time through the analytics results when creating a narrative. "You send an email to the research department, get first results, iterate the process - that usually takes a day or more."

Ravel Law

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.

Apart from prediction technology, Ravel Law 's software also claims to provide lawyers with judges' data on cases, circuits and ruling on their dashboard, which can be used in landing new clients. Currently, the company is bolstering their data minefield by working with Harvard Law School in digitizing the faculty's US case law library to be made available on its tech platform.

Settlement Analytics

It must be noted that legal analytics still has its limitations. Robert Parnell, CEO of SettlementAnalytics, explains in his paper that there is uncertainty in producing results with high level of accuracy with this kind of technology. This comes in very small sample sizes after filtering, cognitive biases, the tendency to interpret random trends as valid patterns, and having a lot of data noise.

Parnell opines, "Overall, the quantitative analysis of legal data is much more challenging and error-prone than is generally acknowledged. Although it is appealing to view data analytics as a simple tool, there is a danger of neglecting the science in what is basically data science. The consequences of this can be harmful to decision making."

Document Automation

A McKinsey & Company report estimates that knowledge work automation will most likely be one of the top disruptors in the global economy.

Some law firms are also beginning to adapt such technology by drafting documents through automated software. Many such software companies claim that the final document, which could take days by manual human drafting, is generated in a matter of minutes. This technology is utilized in finance today; discover real world banking-sector examples of the concepts outlined in this article.

PerfectNDA

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. The user answers questions and a pre-filled template is then generated. In addition, the software also features document filing and integrated e-signatures to streamline related manual processes involved in NDA drafting.

Intellectual Property

Securing patents, copyrights and trademarks is often best left to a lawyer's expertise. However, the entire patent application process can be long and arduous. Traditional trademark and patent search, for example, involves looking into hundreds, if not thousands, of results through manual research . This takes so much time, which is ironic considering that patent applications are time-sensitive.

TrademarkNow

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 system analyzes the results and ranks them according to relevance to the user as identified by the algorithm. As this many e-discovery applications, the solution promises efficiency of spent attention for legal teams.

ANAQUA Studio

The cloud-based ANAQUA Studio, on the other hand, is specifically designed for drafting patents and prosecution. 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. The system is said to be able to detect document errors, circular claim references and formatting defects aside from automatically generating literal claims support.

SmartShell

A brief overview of how the product's functions and value proposition can be seen in the 1-minute video below:

Electronic Billing

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. Firms in the healthcare space are also utilizing AI for medical billing; this concept is further explained in our article Artificial Intelligence for Medical Billing and Coding.

Brightflag

Brightflag offers a centralized legal pricing software that automatically adjusts line-by-line items. It also allows users to centralize the invoice review so that all documents submitted are routed directly to the correct approver. In addition, the AI provides analytics features by tracking and categorizing all pricing data to determine alternative fee arrangements (AFA) and budgets.

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. The company lists telecom giant Telstra and ride-hailing company Uber among its current marquee clients.

Smokeball

Smokeball 's cloud-based legal practice management tool automates the recording of time and activities by law firms. One major feature of this tool is the capability to track all activities including emails that are valid for billing. It claims to have automated more than 600,000 forms and managed over 10 million documents according to its website. The video below explains Smokeball's software:

Unintended Consequences

Concluding Thoughts on AI in Law and Legal Practice

In exploring dozens of AI legal-tech companies and use-cases, there are many looming questions with respect to adoption. In other words: Which firms will use these technologies - and which AI legal-tech applications will become commonplace in the short term?

I'll conclude this article with some thoughts about what might be a bit of a "catch 22" for AI in law and the legal profession.

The most acknowledged benefit of AI tools in legal practice seems to be improving efficiency. AI software employs algorithms that speed up document processing while detecting for errors and other issues.

This seems somewhat counterintuitive as the legal profession has long relied on "billable hours", and it often does not behoove a lawyer to take less time in completing a task or document. For this reason, merely eliminating manual (or boring) tasks will likely not be strong enough to drive AI adoption.

Rather, the pressure to adopt AI will likely come from peer pressure. 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.

It is unclear as to how the transition to legal AI will occur. On the one hand, we might expect large law firms to drive initial adoption as they are most able to pay for robust AI-based tools and integrations. However, newer firms would be most likely be begin with a lean, automated, efficiency-driven approach, because they don't have to deal with the massive existing overhead of larger firms.

Emerj CEO Daniel Faggella believes that broad adoption of AI in law may begin with an ecosystem of small, nimble legal firms will emerge - a group of firms focused from day one on maximal automation and efficiency.

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.

This won't be done through massively high rates for their billable hours, but by charging low rates and scaling their services with automation. 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. Faggella states:

"The emergence of these smaller firms will shock the larger legal players, who will scramble to keep their prices and services competitive - much in the same way that stodgy paper-based newspapers had to adjust to digital publishing.

By in large, they didn't adjust. Instead, a cadre of up-and-comers ate their lunch and most of the stodgy publishers died off or clung on by their fingernails with herculean effort. It's unclear whether a similar shift will be as fast with the legal field as it was with publishing, but the same kind of sea change seems to be underway."

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.

Further insight on how an enterprise should prepare for AI initiatives is detailed in the AI in Industry podcast titled: What It Looks Like to Be Ready for AI Adoption in the Enterprise
Header image credit: Brown & Brown Insurance

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AI in Law and Legal Practice - A Comprehensive View of 35 Current Applications | Emerj

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Artificial intelligence (AI) companies continue to find ways of developing technology that will manage laborious tasks in different industries for better speed and accuracy . In the legal profession, AI has already found its way into supporting lawyers and clients alike.

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An old lady lawyer friend of mine chastised me for my comment in a recent column that, these days, I am discouraging women from going to law school and entering the profession.

Her point is that unless women are in the profession, things will not change. True, but things aren't moving very fast now with women making up more than 50 percent of law school classes, but then, at some later time, ditching the profession for whatever else they may choose to do. Does anyone wonder why?

In support of my position is Elie Mystal's latest rant. (Thank you, Elie.) No, my goal here is not to show him that I read what he writes, but to illustrate yet another reason why I discourage women from entering the profession. It seems that anatomy is still financial destiny in the legal world and that if you don't have certain anatomical characteristics, your likelihood of making the big bucks bonus is 31 percent less. A woman general counsel just isn't valued financially like her male counterparts. What does that say about the concept of "equal pay for equal work"? A sham, empty words, lip service, whatever.

As Elie points out, "[T]he report also reveals that at all levels, female in-house counsel are making far less than their male in-house counterparts. In other roles in the legal department, female in-house counsel made 8.2 percent less than male in-house attorneys in 2016." Wow, isn't it nice to be undervalued like that?

Many years ago at my first in-house job, I was told that I wasn't making as much as a male colleague (who graduated in the "second quintile" of his class, whatever that meant, while I graduated in the top 10 percent of mine) because he had a family. I had a husband, soon-to-be laid off, and was dealing with rebuilding part of our property resulting from disastrous canyon flooding at the same time. Whatever. "Quint" had more value because he was a man. This was in the early 1980s. Should I have complained? What would have happened? Have things changed since then? You tell me.

So, why do I discourage women from entering law these days? If you're not sure, then reread the links above, and then will you really have to ask?

On a more encouraging note, at least right now, I read an article that should give all of us hope that we won't all be replaced by robots, at least in the next couple of years.

Part of the rap on lawyers is that we seem to lack imagination and creativity, that we are completely linear thinkers, and as a result, we think that we don't have a creative bone anywhere in our bodies.

The World Economic Forum's "Future of Jobs Report" lists the top 10 job skills required for workers to succeed by 2020. Here they are: one skill has gone from last to third.

What is it? Creativity.

Do we have it? I think we do, although it's often buried under boilerplate work in contracts, discovery, and other often mind-numbing tasks that consume much of the daily grind. I don't think that anyone, except for a junior puppy, and maybe not even one of those, relishes propounding or responding to discovery or comparing redlined drafts at midnight. Those tasks are necessary, but definitely not the most creative.

Lawyers can be and are creative in a number of ways, but we just don't see ourselves in those terms. Columnist Jeff Bennion agrees. For example, prepping for trial is creative. You have to figure out how to tell a story in the best possible way, whether representing plaintiff or defendant, and how to create interest and empathy in your client's case. Trial definitely requires creativity. You have to think on your feet, never knowing when that curve ball is coming, be it an objection you aren't expecting, a witness who rolls on you (that never happens, right?), or impeachment evidence. You have to grab the jurors by the scruffs of their necks and keep them fascinated, wondering what is going to happen next. Your job as a trial lawyer is storyteller. They snooze, you lose.

Transactional lawyers also can be creative when they think about the best way to structure a particular deal. It's never one size fits all or it shouldn't be. A Lawyerist blog post has some suggestions.

You don't have to tell me that it's hard to turn off your inner critic and just let ideas flow, whether good or bad or somewhere in between. It's our job to see the flaws in an argument, a document, and so to let that go can be very difficult, but letting go of the "we've always done it this way" can lead to creative problem solving. I see this in mediation, assuming that the parties and counsel are open-minded enough and that's not necessarily a given.

When I was in-house at a financial institution, I often represented it in "workout" matters, e.g., "special assets," "loan adjustments," or whatever the terminology was. What was fun about that was advising the workout officer, who was usually much more risk tolerant than the rest of the company. The loan was already in the toilet, so what would be the best way to get repaid in full, take a haircut, or make some other arrangement? Those deals could be very creative.

Yes, automation is here, yes, so is artificial intelligence, but can you and would you even want to program creativity? We need to emphasize that part of a lawyer's practice involves creative thinking. How would a robot handle a recalcitrant witness? Could a robot show empathy to a victim who has suffered a catastrophic injury? Are we headed for a dystopian legal world? Do we want to find out?

If robots are anatomically correct (according to various websites, they are), then we know which will be more valued.
Jill Switzer has been an active member of the State Bar of California for 40 years. She remembers practicing law in a kinder, gentler time. She's had a diverse legal career, including stints as a deputy district attorney, a solo practice, and several senior in-house gigs. She now mediates full-time, which gives her the opportunity to see dinosaurs, millennials, and those in-between interact - it's not always civil. You can reach her by email at oldladylawyer@gmail.com.

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Robots Will Never Be As Creative As Lawyers

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An old lady lawyer friend of mine chastised me for my comment in a recent column that, these days, I am discouraging women from going to law school and entering the profession. Her point is that unless women are in the profession, things will not change.

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Many legal professionals currently use artificial intelligence (AI) in their work, although they may not always realize it. Even among the most tech-savvy attorneys, questions remain as to what AI means for the legal profession today - and in the future.
Three of the most common questions include:

What is the definition of AI and how does it differ from other types of technology?

How will advances in AI change the way legal professionals work in the future?

And, perhaps most importantly:

How do you know when AI technology can be trusted in the legal space?

In this post, Thomson Reuters Westlaw shares answers to these questions based on the perspectives of our experienced attorney-editors and technology experts.

What does artificial intelligence really mean?

Is AI just a meaningless marketing buzzword? Robots trying to take over the world? Different people in a variety of industries have different concepts of what AI is and what it does. Therefore, any useful discussion of AI has to begin with a common understanding of the term.

AI is sometimes defined as the simulation of human thought processes in a computerized model. For Westlaw developers, that means teaching computers to mimic human behavior and thoughts in order to find the most relevant and accurate results, saving legal professionals time without sacrificing confidence.

One good example is the use of Natural Language search in Westlaw. Readers who have been using Westlaw technology since the early 1990s may recall a time when it was necessary to utilize Boolean Terms and Connectors in order to surface meaningful search results. But then Westlaw implemented Natural Language search capabilities allowing the computer to recognize the way humans naturally think and use language.

Introducing natural language technology in Westlaw meant that instead of humans having to think like machines, machines were taught to operate the way humans think. This made searching within Westlaw easier and faster for users.

Another way to define AI is "whatever a computer can't do today." When an idea for an AI innovation is new, or sounds impossible, then it is referred to as AI. But once it's commonplace, it's considered to be just another piece of software - even when it continues to achieve the same simulation of human thought processes that it always did. Think about Natural Language search: It was a huge technological advance but today is built into most databases and search tools. Natural Language search isn't called "AI" anymore; it's simply expected.

What does the future hold for attorneys as AI advances?

A huge topic of analysis and debate across industries is whether, how, or when artificial intelligence may eliminate jobs and displace people. The legal industry is no different.

On one end of the spectrum are those who worry that their jobs may be in jeopardy in the near term because technology will replace them. In the legal world, however, the perspective on the opposite end of the spectrum may be more common:

If you think computers are going to replace lawyers, then you don't understand what lawyers do.

The law presents unique challenges compared to other industries when programming computers to simulate human thought. In matters of the law, there is rarely a single mathematically/objectively correct answer to a particular question.

Lawyers exist because of nuance and disagreement. Fine points related to language (even punctuation) can mean the difference between a desired outcome and its opposite. The unpredictable human elements of the law - judges, clients, witnesses, juries - along with its constantly changing nature and the unique fact patterns of each individual case all combine to mean that legal work will remain a human business.

Jill Switzer points out two examples of much-needed, higher-thinking legal work that a computer can't accomplish - creative storytelling and reacting in real time to changing circumstances in a trial setting - in her post on the Above the Law blog, "Robots Will Never Be as Creative as Lawyers."

In order for skills like creativity and thinking on one's feet to become obsolete in the legal world, we would first need to see either a major revolution in the way our legal systems operate, a massive leap forward in technological innovations, or both. Given all of that, attorneys are not likely to be replaced by computers anytime soon.

While technology can't yet achieve all of what a lawyer must do, it can help free up legal professionals' time and brainpower so more is available for creative thinking and strategy. There is a lot of opportunity to automate legal tasks that are repetitive and rote, like e-discovery and document review.

Legal research is another area where AI can augment the work of attorneys.

Westlaw has a long history of understanding what is most relevant to legal professionals when they are conducting legal research. That allows us to create AI enhancements like Research Recommendations and Folder Analysis.

One time-saver for Westlaw users is Research Recommendations, which uses your current legal research session to recommend additional cases, Key Numbers, and statutes related to the issue you're searching.

Similarly, Folder Analysis analyzes cases you place in your folders to determine the common legal issues present. It will display a breakdown of those issues and also recommend additional cases based on the issues identified.

Working closely with experienced attorney-editors, the technical experts behind Westlaw design these enhancements based on data about customer needs, in-depth knowledge of the legal research process, and analysis of common legal questions and issues.

Westlaw enhancements aren't designed to replace the work of attorneys as they synthesize and apply their research to each individual case. Rather, these AI-based enhancements speed your work and add confidence to your research process so you can spend your time and energy on other creative and intellectual tasks - tasks that only a human can do.

User feedback on Westlaw enhancements is overwhelmingly positive about its potential to make the research process faster. However, an understandable and frequent follow-on question from users remains: How do I know I can trust it?

How do I know I can trust AI technology for legal work?

By nature, attorneys are questioners, trained to consider every angle and potential outcome of every argument. You are thorough in your research and never satisfied that you've completed your work. How, then, can you know when to trust technological tools to aid you in your tasks? This especially applies when you don't necessarily see what the algorithms and assumptions are that led the technology to produce a particular result or recommendation.

It's crucial for legal researchers (inevitably also AI users) to have knowledge of and confidence in the people and processes behind the technologies on which they rely. Machine learning is only as good as the people who design, train, test, and refine it. Good AI feels natural to the user. It creates a streamlined experience, but that's only because, behind the scenes, the technology is underpinned by high-quality data and experience.

Behind the Westlaw AI functionality is a team of knowledgeable attorneys, data scientists, technologists, and designers, all of whom work closely together. Attorney-editors go through extensive training to ensure consistency and accuracy when it comes to how users' research is organized and presented in Westlaw. Their legal knowledge and standards are similarly applied when they collaborate with product developers to create and refine AI enhancements. All of these elements are brought together to create a clean user interface that ensures maximum efficiency for legal researchers using Westlaw.

Conclusion

Westlaw attorney-editors, data scientists, and technologists will continue to work with our customers to innovate and find ways to augment attorneys' work through artificial intelligence. Our goal remains the same: to help legal researchers work faster and have more confidence in their results so they can apply their talents, creativity, and expertise to the legal tasks that only humans can accomplish.

Ready to try Westlaw Edge?

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Many legal professionals currently use artificial intelligence (AI) in their work, although they may not always realize it. Even among the most tech-savvy attorneys, questions remain as to what AI means for the legal profession today - and in the future.