Data analytics and artificial intelligence in litigation
Vol. 78, No. 1 / Jan. - Feb. 2022
Nan L. Grube
Nan L. Grube is associate general counsel at Edward Jones responsible for the information and data privacy reporting to the firm’s CPO.
“Do I have a good case?” asks every client that walks into the office. What the client is truly asking, however, is for the lawyer to predict the outcome and cost of the case during all phases of litigation.2 Adding data analytics and artificial intelligence (AI) to the management of litigation can greatly assist a lawyer in predicting results and financials.
Perhaps most importantly, coupling data analytics and AI with traditional litigation management can dramatically assist both legal departments and law firms in determining whether there is value in proceeding with litigation.3
Assessing the practicality and sustainability of a piece of litigation is an ongoing cost-benefit analysis of interrelated and ever-evolving variables. The lawyer analyzes the risks presented by the tangle of short deadlines, unfavorable judicial assignments, procedural rules, opposing party personalities, client nuances, and finances that may sway the strategic direction of a matter, not to mention the facts, legal precedents, statutes, and regulations that directly impact the case outcome.4 Moreover, the pressure cooker includes constantly assessing the viability and value of the litigation, successfully meeting the client’s expectations of a “win” while adhering to a lean budget, and routinely presenting it in a manner that is understandable for the client.5 No easy task.
In the past, lawyers based the outcome and cost prediction of a case upon their legal training, years in practice, and exchanges among colleagues of anecdotal experiences in a jurisdiction or before a specific judge.6 Perhaps as a manner of protecting one’s reputation, any prediction was likely caveated with, “But we never know what a court or jury will do.”
In today’s practice, effective litigation managers can supplement their legal training and years of practice with enhanced insight from strategic and proactive use of quantitative predictions derived from statistical patterns and practices uncovered by data analytics and AI.7 Lawyers utilizing this technology have enhanced value to clients because of their increased ability to accurately predict costs and outcomes.8 The technology is readily available and priced for the general consumption. The costs can range from free (Google Scholar for searching scholarly articles and Fastcase, which is available at no cost to lawyers through The Missouri Bar) to relatively expensive and comprehensive (full-service Westlaw, Lexis, and Bloomberg), although even these dedicated services have plans that add value.
The use of data analytics and AI has been a “game changer” for society.9 It is busy in the background of our everyday lives — from the airplanes that fly on autopilot10 and that can now land on their own,11 to product recommendations from Amazon.12 However, the legal field remains somewhat sluggish in its adoption of AI and data analytics that will keep it profitable, agile, and in step with its business partners.13
Perhaps the lack of adoption is due to lack of understanding what AI and data analytics are and how they work. Generally speaking, data analytics is discovering the patterns, trends, and relationships between and among a colossal amount of data through the use of computer algorithms and programs.14 Data analytics uncovers patterns that correlate with an outcome, and in the legal field, this data allows lawyers to make informed strategic decisions.15 Examples might include finding the case on point in a water rights matter between states, knowing that a judge has ruled in favor of the plaintiff 45% more often than the defendant in a bench trial setting, or knowing that opposing counsel has filed 76% of all litigation in a particular county in the employment law area.
AI, as the term was first coined in 1956, is a complex and varied area of computer science.16 AI is an umbrella term that includes an array of technologies that learn over time as they are exposed to more data.17 AI embraces the capability to learn, reason, and understand concepts and relationships.18 The bulk of today’s AI uses human reasoning as a guide to provide a better service or create a better or more useful product.19
Utilizing AI and data analytics in the law is changing the way lawyers practice.20 More than any technology that has preceded it, AI has the ability to transform the practice of law in remarkable ways.21 Still, AI isn’t truly new but has been discussed in the legal field since the 1970s.22 Nonetheless, it has been slow to be adopted widely. So, where is the legal field using it?
AI is used in discovery, for one. Many, if not most, lawyers are familiar with e-discovery and Technology Assisted Review (TAR). TAR is the process of having the computer software (AI) classify the documents of a discovery review based upon a set standard from expert reviewers.23 TAR can dramatically reduce the time and cost of review24 and has become commonplace in litigation. Several courts have even required TAR in certain cases.25
Electronic legal research is another area that is steeped in AI and will continue to improve as continued investment in AI technology is made.26 Historically, legal research was tied to expensive books with archaic indexing systems. In fact, a portion of us in practice learned to research in the actual hardbound books in the stacks of the law library. But a growing number of us have never opened a court reporter and instead sat down at a computer that may or may not have been in the law library to complete a Boolean search to study a legal topic while in law school. Now, law firms and law schools have limited the books or even removed most of them and invested instead in computers and subscriptions services that are powered by AI. For example, popular databases like Lexis, Westlaw, Casetext, Fastcase, and Google Scholar have integrated AI. Primarily, the well-known law research platforms are based in the federal system because it is completely digitized on PACER. For example, Lexis offers increasingly robust litigation analytics for cases, judges, and litigants in federal courts, allowing the litigation manager to discover more about a particular federal judge or lawyer practicing in federal court. However, powerful litigation analytics is not limited to federal court; as more states become digitized and adopt e-filing, a boon in state-based analytics and predictive technologies will follow for state jurisdictions as well.27 A company called Gavelytics is providing an AI-powered analysis of tens of millions of state court litigation documents to find behavior patterns of judges, law firms, litigants, and motion filings in at least 10 state jurisdictions with plans to cover 20 states.28 Through AI, Gavelytics can help lawyers discern the judicial leanings, speed, rulings by motions and outcomes, and bench trial tendencies; it can highlight information about the opposition concerning its case filings and outcomes; and it can also review detailed filing by the litigants and the win/loss ratio.29 Fastcase touts an AI sandbox that is fully customizable for user-driven data analysis projects, and lawyers can utilize Fastcase’s data to bolster research and analytics projects (additional costs may apply).30
AI has increased the lawyer’s ability to anticipate and predict a litigation’s trajectory based upon the historical path of previously amalgamated litigation.31 It is even used to predict the eventual litigation outcome.32 Several startup companies are building models to predict the outcome of pending cases.33 Blue J Legal, for instance, is a startup with an AI-powered prediction engine focused on tax cases and employment law cases. According to Blue J, the system works by using machine learning to predict how a court would rule in a specific scenario. Lawyers can then input the scenario by filling out a brief questionnaire with facts about the unique legal situation. Blue J uses its AI to compare the entered case against all relevant previous cases in its database. From this point, a lawyer can simulate a change in facts on the outcome and compare it against other cases.34 Blue J reports it can predict case outcomes with 90% accuracy.35 This information can be used in litigating, settling, or presenting the case and the possibilities to the client.
In the area of litigation finance, where a third party finances the plaintiff’s litigation costs in return for a share of the successful outcome, AI is creating a data-driven assessment to determine which cases are worthy of investing. Similar to Blue J, Legalist is using data from 15 million court cases from all over the United States to predict which lawsuits are likely to be winners based on historical data utilizing criteria such as the length of the litigation and the probable amount of settlement or judgment.36 The information allows investors to make sound judgments about which cases are worth the investment.
Preparing litigation is benefited from adding data analytics and AI because it can provide quick, efficient, and accurate results that would be wholly inefficient if undertaken by a group of associates or paralegals. For example, AI and data analytics can make a determination of the important facts in relevant documents by finding and highlighting words and phrases used repeatedly or in combination with one another; determine and set forth the timeline of events derived from emails and document creation as determined by the metadata; create a visualization of the spider web of connected emails and conversations between relevant parties based on who corresponded with whom about what subject using which phrases; determine important fact witnesses in global litigation based on the number of communications or how frequently a person was involved in the discussion; reveal key terms, including short form or anacronyms; unveil case themes; and highlight outcomes of similar cases. With the press of a button or the crunch of a software program running through a database, data analytics are pushed out to unearth new information the litigation manager can use in the specific case.
The litigation manager’s strategy decisions can benefit from AI and data analytics as well. Litigation data analytics can facilitate the discovery of the best arguments, tactics, and cases to use before a specific judge based upon the judge’s past rulings and behavior.37 Litigation teams can find and track successful strategies in previous matters and quantitatively assess litigation risks and likely outcomes.38 For example, the litigation manager can access the metrics of several potential jurisdictions to determine which is the most advantageous for filing. Data analytics and AI can issue and track litigation holds within a corporation, highlight strengths in outside counsel, expose weaknesses in opposing counsel, reveal an opponent’s propensity to settle early, and unearth a judge’s preferred local counsel.39 If litigation managers have access to and knowledge of the facts and strategies that have worked in the past, then they can make similar decisions going forward.
While it is true that each of these areas can be researched and managed by lawyers using traditional methods, the time and dollars expended are dramatically increased,40 perhaps to a point that would make it unfeasible. Further, the insight may come so late in the litigation cycle that it would no longer be useful or pertinent. The cost and time savings of data analytics and artificial intelligence cannot be disputed, even though its use is relatively new to the practice of law.41 Quickly sorting through hundreds of thousands of emails, delivering pertinent cases in a fraction of the time it would take a seasoned researcher to locate them in reporters, and illuminating the patterns and propensities of the sitting judge are possible due to artificial intelligence.42
Use of AI and data analytics is already pervasive in the business world because it yields efficiencies, predicts behavior, and discovers trends to gain a competitive advantage in the global market place.43 Clients are demanding the same cutting-edge approach to their legal challenges to bring maximum value and deploy appropriate resources.44 By utilizing rigorous statistical data in baseline case predictions, litigation managers can dramatically improve outcome predictions and decisions – and produce tailored client advice.45
Lawyers now have statistical data to support their anecdotal experience, or proverbial “gut feeling,” with legal data analytics and AI.46 Data-driven predictions are 60% more accurate than when a lawyer acts based on years of experience or “gut feeling” alone.47 Ultimately, legal departments and law firms can determine if there is value in proceeding with litigation based on the data analytics sourced from AI.48
Still, as noted, the legal field has been slow to adopt the technology.49 Traditionally, the legal field has been predicated upon taking a labor intensive approach to solving a client problem to produce a superb legal product with limited thought to the cost.50 In contrast, technology produces efficiency, but efficiency does not support traditional legal hierarchical firm structure.51 Conversely, corporate general counsels have been pressured to operate with the same efficiency, speed, and value as their business counterparts and are likewise demanding the same of outside firms retained to handle the litigation portfolios.52 The average business wants to extract maximum value from its deployed resources, and its outside counsel are not immune.53 Therefore, technology can assist firms in meeting client needs, but the firm will also need to review its traditional hierarchical structure.
AI and analytics are a mainstay in the business world54 and will become commonplace in the practice of law.55 Lawyers will need to utilize data analytics and AI for every facet of preparing, understanding, and litigating a matter.56 Data is the great equalizer among lawyers, providing each person with the same information.57 However, willingness to adopt data analytics and AI in the litigation management space will be the differentiator among law firms.58 In other words, the synergy of the lawyer using data is greater than the data alone or the lawyer alone.59
It’s clear the legal field sees technology’s potential — 78% of law firms and 80% of corporate lawyers see the greater use of technology as one of the biggest changes in legal service delivery in the next three years.60 However, while 45% of general business companies have hired a technology specialist or a team of specialists, only 27% of law firms have done so.61 With only 27% of law firms investing in the technology necessary to gain these efficiencies, there will be a divide among those firms that harness the technology and those that sit on the sidelines.62
Data is available to anyone willing to mine it and utilize it, thus leveling the playing field among the high-priced white glove lawyer and the smaller boutique lawyer. As technology continues to mature and the players in the space multiply, pricing becomes more and more competitive, making it affordable to most. For those who find these technologies outside of their budgets, there are free options noted above, and most law libraries now have access to legal research at the minimum.
Law firms utilizing this technology will be able to achieve better outcomes through insightful data-driven decisions. Leveraging the technology available to lawyers has become a strategic advantage, and those not positioning themselves to take advantage of the technology will likely be left behind. Coupling data analytics and AI in litigation preparation allows the efficient and informed litigation manager to deploy the appropriate resources to extract the maximum value for the client and provide answers to, “Do I have a good case?”
1 Nan L. Grube is associate general counsel at Edward Jones responsible for the information and data privacy reporting to the firm’s CPO.
2 Marl K. Osbeck, Lawyer as Soothsayer: Exploring the Important Role of Outcome Prediction in the Practice of Law, 123 Penn St. L. Rev. 43 (2018).
3 Edward Bird, Case Prediction Analytics: Enhancing the Litigator’s Armoury, Internet Newsletter for Lawyers (June 2019), https://www.infolaw.co.uk/newsletter/2019/06/case-prediction-analytics-enhancing-litigators-armoury/.
4 ABA Model Rules of Professional Conduct: Preamble & Scope, Preamble: A lawyer’s responsibilities , https://www.americanbar.org/groups/professional_responsibility/publications/model_rules_of_professional_conduct/model_rules_of_professional_conduct_preamble_scope/ (stating the attorney’s core function in litigation is to evaluate the facts, the law, and the outside factors which may impact the litigation).
6 Bird, supra note 3.
7 Daniel Martin Katz, The 2012 Randolph W. Thrower Symposium Innovation For the Modern Era: Law, Policy, and Legal Practice in a Changing World: Article: Quantitative Legal Prediction - or - How I Learned to Stop Worrying and Start Preparing For the Data-Driven Future of the Legal Services Industry, 62 Emory L.J. 909, 928 (2013).
8 Osbeck, supra, note 2
9 Ori Ioannou, Jim Goodnight, the “Godfather of A.I.,” predicts the future fate of the US workforce, CNBC (Nov. 4, 2019), https://www.cnbc.com/2019/11/04/godfather-of-ai-predicts-the-future-fate-of-the-us-workforce.html.
10 Chapter 4, Automated Flight Control, Advanced Aviation Handbook, Federal Aviation Administration, https://www.faa.gov/regulations_policies/handbooks_manuals/aviation/advanced_avionics_handbook/media/aah_ch04.pdf.
12 Rhonda Bradley, 16 Examples of Artificial Intelligence (AI) in Your Everyday Life, The Manifest Blog (Sept. 26, 2018) https://medium.com/@the_manifest/16-examples-of-artificial-intelligence-ai-in-your-everyday-life-655b2e6a49de.
13 Katz, supra note 7.
14 Bird, supra note 3.
15 Bird, supra note 3.
16 Bernard Marr, The Key Definitions of Artificial Intelligence (AI) That Explain Its Importance, Forbes (Feb. 14, 2018.)
17 Ellen M. Gregg, Bill Koch, and Daniel W. Smith, How Artificial Intelligence Is impacting Litigators, ALAS Loss Prevention Journal, Summer 2019, at 48.
19 Marr, supra note 17.
20 Ready or Not: Artificial Intelligence and Corporate Legal Departments, Thomson Reuters, https://legal.thomsonreuters.com/en/insights/articles/artificial-intelligence-ai-report (last visited October 17, 2019).
22 Bruce G. Buchanan & Thomas E. Headrick, Some Speculation About Artificial Intelligence and Legal Reasoning, 23 Stan L. Rev. 40 (1970).
23 Technology Assisted Review, EDRM, https://edrm.net/resources/frameworks-and-standards/technology-assisted-review/ (last visited May 7, 2021).
25 Julia Voss and David Simmons, Technology Assisted Review Makes Main Street, A.B.A. Sec. of Litig. (Aug 30. 2018), https://www.americanbar.org/groups/litigation/publications/litigation-news/technology/technology-assisted-review-makes-main-street/.
26 Timothy Fox, Viewpoints: A Journey from Law to Tech, Law Street (April 14, 2021), https://lawstreetmedia.com/viewpoints/viewpoints-a-journey-from-law-to-tech/.
30 Legal Aata API, Fastcase, https://www.fastcase.com/solutions/legal-data-api/ (last visited Dec 17, 2021).
31 Osbeck, supra note 2 at 61.
32 Ellen M. Gregg, Bill Koch, and Daniel W. Smith, How Artificial Intelligence Is impacting Litigators, ALAS Loss Prevention Journal, Summer 2019, at 48.
33 Rob Toews, AI will transform the field of Law, Forbes (Dec. 19, 2019).
35 Toews, supra note 35 .
36 Hiawatha Bray, Legalist brings big data to small lawsuits, The Boston Globe (Sept. 4, 2016), https://www.bostonglobe.com/business/2016/09/04/legalist-brings-big-data-small-lawsuits/35P9avVWkZca2b5WUDAc1J/story.html (last visited May 7, 2021).
37 Bird, supra note 3.
38 Brian Dalton, Big Data and the Litigation Analytics Revolution, Above the Law 2020, https://abovethelaw.com/law2020/big-data-and-the-litigation-analytics-revolution/?rf=1 (Last visited October 6, 2019); Bird, supra note 3.
39 Bird, supra note 3.
40 Dan Panitz and H. Bruce Gordon, Reaching Critical Mass In the Land of Build, Buy or Rent, Corporate Counsel (Aug. 26, 2019) https://www.law.com/corpcounsel/2019/08/26/reaching-critical-mass-in-the-land-of-build-buy-or-rent/?slreturn=20190916124227.
41Ready or Not: Artificial Intelligence and Corporate Legal Departments, Thomson Reuters: The Legal Department 2025, at 12, https://static.legalsolutions.thomsonreuters.com/static/pdf/S045344_final.pdf (last visited Oct. 17, 2019).
42 Lindahl, supra; Nicholas M. Pace and Laura Zakaras, Where the Money Goes: Understanding Litigant Expenditures for Producing Electronic Discovery. RAND (2012), https://www.rand.org/content/dam/rand/pubs/monographs/2012/RAND_MG1208.pdf.
43 Patrick Flanagan and Michelle Hook Dewey, Where Do We Go From Here? Transformation and Acceleration of Legal Analytics in Practice, 35 Ga. St. U.L. 1245, 1246 (2019); Market Research Future, Big Data Analytics Market 2018 Global Size, Share, Growth Opportunities and Industry Forecast by Type, Price, Regions, Key Players, Trends and Demand by 2023, Herald Keeper (Aug. 27, 2018), https://heraldkeeper.com/market/big-data-analytics-market-2018-global-size-share-growth-opportunities-industry-forecast-type-price-regions-key-players-trends-demands-2023-104850.html.
44 Mark A. Cohen, What does “More With Less” Mean For The Legal Industry?, Forbes (Aug. 6, 2019), https://www.forbes.com/sites/markcohen1/2019/08/06/what-does-more-with-less-mean-for-the-legal-industry/#5a6520d15c2f.
45 Bird, supra note 3.
46 Osbeck, supra note 2, at 59.
47 Bird, supra note 3.
48 Bird, supra note 3.
49 Thomson Reuters, supra note 21, at 14.
50 Cohen, supra note 45.
51 Cohen, supra note 45.
52 Cohen, supra note 45.
53 Cohen, supra note 45.
54 Ioannou, supra note 9.
55 Dalton, supra note 38; Thomson Reuters, supra note 21;, Osbeck, supra note 2.
56 Steph Wilkins, Litigation in the age of Big Data: How Everlaw is Tackling the Most Complex Technical Issues in eDiscovery, Above the Law (Jan. 24, 2019), https://abovethelaw.com/2019/01/litigation-in-the-age-of-big-data-how-everlaw-is-tackling-the-most-complex-technical-issues-in-ediscovery/.
57 Katz, supra at 7.
59 Katz, supra at 7.
60 Survey, The Future Ready Lawyer, Wolters Kluwer at 22 (2019).
61 Id. at 18.