By Michael Mills, co-founder and chief strategy officer, Neota Logic
The other day, a search for “artificial intelligence in law” produced 86,400 results from just the News section of Google’s vast index. From the Web as a whole, 32,800,000 results and from Videos – 261,000, beginning with Jude Law’s role as Gigolo Joe in the movie A.I. (thank you, RankBrain).
The first News story was “Law firm bosses envision Watson-type computers replacing young lawyers,” reporting on the answers to one question in the recent Altman & Weil survey of law firm leaders (page 82). As wittily argued by Ryan McClead, “the question is flawed on many levels [and] … it’s time to cut the hysteria surrounding artificial intelligence in law.”
Yes, there’s something going on here. But we need to parse the pile a bit. What is artificial Intelligence (“AI”)? What is AI doing in law? Who is doing it? And where is it headed?
What is this thing called AI?
AI is a big forest of academic and commercial work around “the science and engineering of making intelligent machines,” in the words of the person who coined the term artificial intelligence, John McCarthy. A thorough and hype-free review of AI in business was published recently by Deloitte, Demystifying Artificial Intelligence, suggesting the term “cognitive technologies” to encourage focus on the specific, useful technologies that emerge from the broad field of AI.
However labeled, the field has many branches, with many significant connections and commonalities among them. The most active today are shown here [click to enlarge]:
Lawyers do not need robots or machine vision, but other branches of AI are indeed useful. Practical use of cognitive technologies in legal services is by no means new, nor did it begin when IBM Watson won Jeopardy! or when IBM’s general counsel predicted that Watson could pass the bar exam by 2016.
What’s happening today?
Legal research—Lexis and Westlaw have applied natural language processing techniques to legal research for 10+ years. No doubt Bloomberg BNA does as well. After all, the core NLP algorithms have all been published in academic journals and are readily available. The hard (very hard) work is practical implementation against good data at scale. Legal research innovators like Fastcase and RavelLaw have done that hard work, and added visualizations to improve the utility of results.
This year, ROSS Intelligence has been applying IBM Watson’s Q&A technology to legal research on bankruptcy topics, after winning a finalist spot in an IBM Cognitive Computing Competition among 10 universities. After building and training the data set, ROSS invites users to evaluate search results, and feeds those evaluations back to the engine to continue tuning (the essence of machine learning) in the manner of recommendation engines at Netflix and Amazon as well as Google’s feedback loops based on what we do with the search results we’re shown.
Last month, Thomson Reuters, publishers of Westlaw, announced a collaboration to use Watson across TR’s information businesses. Although nothing has been said publicly about TR’s specific plans for Watson, one can speculate that the vast trove of legal content in Westlaw and the army of subject matters experts in the company could together do impressive things to improve legal research. Watson needs big data and training, at least initially by people: TR has both.
Document automation—HotDocs, Exari, and Contract Express apply procedural rules and some inferencing to generate legal documents. Not fancy, not new, but algorithmic and very useful.
E-discovery—Technology-assisted review (“TAR” or predictive coding) uses natural language and machine learning techniques against the gigantic data sets of e-discovery. Recommind, Equivio (now part of Microsoft), Content Analyst, and many other vendors develop or license these tools. TAR has been proven to be faster, better, cheaper, and much more consistent than HPR (Human Powered Review). See, for example, Cormack & Grossman, Evaluation of Machine Learning Protocols.
Yes, it is assisted review, in two senses. First, the technology needs to be assisted; it needs to be trained by senior lawyers very knowledgeable about the case. Second, the lawyers are assisted by the technology, and the careful statistical thinking that must be done to use it wisely. Thus, lawyers are not replaced, though they will be fewer in number.
In scale and impact on costs, TAR is the success story of machine learning in the law. It would be even bigger but for the slow pace of adoption by both lawyers and their clients.
Outcome prediction—Lex Machina, after building a large and fine-grained set of intellectual property case data, uses data mining and predictive analytics techniques to forecast outcomes of IP litigation. Recently, it has extended the range of data it is mining to include court dockets, enabling new forms insight and prediction. For example, the Motion Kickstarter enables:
“attorneys [to] view granted motions with denied motions to see what’s working and what’s not. Enter a judge’s name and motion type and instantly view the judge’s recent orders on that motion type, as well as the briefing that led up to those orders.”
LexPredict has built models to predict the outcome of Supreme Court cases, at accuracy levels challenging experienced Supreme Court practitioners. Perhaps Huron’s Sky Analytics and the new AIG spinoff, Legal Operations Company, can use their big databases of law firm case and billing data to offer outcome predictions as well as cost and rate benchmarks.
Self-service compliance—Neota Logic applies its hybrid reasoning platform, which combines expert systems and other reasoning techniques, including on-demand NLP and machine learning, to provide fact- and context-specific answers to legal, compliance, and policy questions.
ComplianceHR, a joint venture of Littler Mendelson and Neota Logic, offers a suite of Navigator applications to assist human resources professionals in evaluating independent contractor status, overtime exemption and other employment law issues. Foley & Lardner uses expert systems technology to power its Global Risk Solutions service, an “integrated FCPA compliance solution that addresses each of the ‘hallmarks’ of an effective anti-corruption compliance program identified” by the regulatory authorities.
Contract analysis—Contract Standards, eBrevia, Kira Systems, LegalSifter, Seal Software, and others apply natural language and machine learning techniques to aspects of the contract lifecycle from discovery to due diligence.
General counsel recognize that their high priorities of risk management and cost reduction are served by understanding and managing the rights, obligations, and risks in a company’s contracts, and rationalizing the processes by which contracts are initiated, negotiated, drafted, and managed through their lifecycle from execution to expiration.
Contract analytics is well on the way to being a success story for machine learning in the law. For example, Kira Systems, reports that contract review times in the due diligence context can be reduced by 20–60%. And Contract Standards can benchmark every provision of a draft contract against industry and company or firm standards in moments. [click on the graphic below to enlarge.]
Is it time to get in the game?
Many, perhaps most, law firms choose not to be early adopters of new technologies. Likely, that is not because they have read about the rewards of being a “fast follower” instead of a “first mover.” Rather, they are lawyers—educated to precedent, alert to their peers, wary of failure and hence reluctant to experiment.
However, as I hope this quick tour has shown, notwithstanding the chatter and excitement about the arrival of Watson in Law Land, the techniques of cognitive technologies are robustly at work in the trenches of law practice, doing useful work today—improving service to clients, reducing costs, creating new opportunities for firms.
The future?
More, and better, of course. Cognitive technologies in the law are riding a wave of ever-smarter algorithms, infinite scaling of computer power by faster chips and cloud-clustered servers, intense focus by companies led by seasoned experts, and ever-greater demand from clients for cheaper, faster, better services.
Note that cheaper is only one of the three words. Faster is important—companies measure cycle time, time to market, and other indicia of speed throughout their businesses, and increasingly expect their lawyers to do the same. And better is critical—big companies face ever-growing regulatory and operational complexity, for which traditional legal services on the medieval master craftsman model are simply inadequate. To meet those needs, only technology-enabled services will do the job. And artificial intelligence is driving those changes.
Michael Mills is the co-founder and chief strategy officer of Neota Logic Inc., developers of a no-code software platform with which non-programmers can build expert systems to automate advice, documents, and processes.