AI and e-disclosure: beyond predictive coding



In e-disclosure, discussions about ‘artificial intelligence’ generally focus on predictive coding – the machine learning process by which case experts teach software to locate relevant information, thereby reducing the time human reviewers must spend doing so.

Industry studies have shown that with the right training, predictive coding achieves better and more cost-effective results than the more traditional, Boolean logic-based approach, which requires humans to give detailed, specifically structured instruction sets for searches. The influence of algorithm-reviewed productions is an issue generating many column inches as commentators look to better understand its role.

Big Data, litigation and artificial intelligence

In truth, this is only the beginning. Just as the original impetus for electronic disclosure was the transition from storing data primarily in hard copy form to storing it electronically, the current trend toward more intelligent AI is being driven by the systemic transformation from primarily structured data retention to much larger, ever-growing, predominantly unstructured data sets.

Much of an organisation’s data universe is potentially discoverable in litigation, but its size and unstructured nature mean that it is now essentially impossible for humans to sort through it without the help of intelligent tools. Predictive coding is a good start, but it still requires too much human intervention to ensure the results are neither over-inclusive nor under-inclusive.

Big Data: a boon for machine learning?

Fortunately, ‘Big Data’ has turned out to be a boon for machine learning. Unlike people, machines are not great at extrapolating meaningful information or detecting macro-level patterns from small datasets. However, Big Data is providing real-world samples at the scale necessary to train AI effectively.

Now, neural network architectures are enabling parallel computations, which work in synchronous and dynamic ways – more like how thinking works in biological brains. Natural language processing algorithms have improved greatly, enabling users to ask richer, more semantically complex questions instead of worrying about syntax for the computer’s sake. A famous example is Watson, IBM’s question-answering supercomputer and Jeopardy! champion. Watson’s cognitive computing system can adapt its learned skills to other contexts and it now delivers cloud-based concierge services, medical diagnostics and even legal services. Last year, NextLaw Labs introduced the Watson-powered service ROSS, as “the world’s first artificially intelligent attorney”.

Artificially intelligent lawyers

Giving legal advice requires human judgment that machines do not have (yet). Nevertheless, ROSS is able to carry out sophisticated work that we would normally assume required the ability to form a ‘judgment’, for example, identifying legal issues from hearing facts in natural language and researching possible answers. Work is underway to build more complete multi-sensory cognitive media platforms, which would render audio and video content that can be searched for. “Objects, faces, licence plates, logos, phrases, sentiment, voice identification and translation, plus additional capabilities that are constantly evolving”, according to a report in

Smarter e-disclosure

If cognitive computing continues to evolve, conceivably we could one day be living in a world where a lawyer could simply ask a natural language question like “did any company officer tell outsiders about the bankruptcy before the stock price fell?”, and a supercomputer would return a wealth of audio, video, text, GPS, timekeeping and other data that might show interactions suggesting insider trading.

But for now, human lawyers still have to make judgments about the information that computers retrieve and they still have to ask the right questions. In fact, when their machine is smart, self-correcting and self-improving, the human team can focus on asking richer questions and developing the deeper story of the case.

The goal is not to get technology to do everything humans can do, but rather to limit the time and effort humans must invest in getting technology to do what humans cannot do very well, like scanning and sifting through terabytes of data. This frees up people to do the deeper intellectual labour that is more valuable to their clients.


Matthew Grant is the director of consulting services at Epiq.

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Matthew Grant


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