Artificial Intelligence in Law: The State of Play 2016 (Part 3)

Topics: Artificial Intelligence, Data Analytics, Efficiency, Law Firms, Legal Innovation

artificial intelligence

The first post in this series discussed current developments in Artificial Intelligence (AI) generally and its application to law. In the second post, we took a closer look at how legal research and ediscovery are being impacted by AI; and in this final installment, we’ll take a look at other AI tools and companies at work in areas of law today.

Artificial intelligence is hard at work in the law — for example, in case prediction, compliance, contract analysis and document automation — though often there is no “AI Inside” label on the box.

Outcome Prediction

Lex Machina, after building a large and fine-grained set of intellectual property (IP) 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 of 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. Premonition says they are using data mining and other AI techniques “to expose, for the first time ever, which lawyers win the most before which Judge.”

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 artificial intelligence techniques, including on-demand natural language processing (NLP) and machine learning, to provide fact- and context-specific answers to legal, compliance and policy questions. (Disclosure: I am Co-Founder and Chief Strategy Officer of Neota Logic.)

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 [Foreign Corrupt Practices Act] FCPA compliance solution that addresses each of the ‘hallmarks’ of an effective anti-corruption compliance program identified” by regulatory authorities.

Contract Analysis

General counsels 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.

Natural language processing, machine learning, and other AI techniques are being applied to many aspects of the contract lifecycle, including discovery, analysis, and due diligence.

For example:

  • Kira Systems reports that contract review times in due diligence can be reduced by 20–60%.
  • KM Standards can “identify common clauses, agreement structure, standard clause language, and common clause alternatives” in a set of contracts.
  • RAVN’s cognitive computing platform, the Applied Cognitive Engine (ACE to its friends), will “read, interpret, and summarize” key information from contracts.
  • Seal Software can crawl a network to discover, and then classify, all of a company’s existing contracts.

Contract analytics is well on the way to being a success story for machine learning in the law.

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 and 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 an 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.