information management

Anyone following artificial intelligence in law knows that its first great cost saving has been in the area of document discovery. Machines can sort through duplicates so that associates don’t have to read the same document seven times, and they can string together thousands of emails to put together a quick-to-read series of a dozen email chains. More sophisticated programs evolve their ability with the help of human input.

Law firms are already saving their clients millions in adopting the technology. It’s bad news for the lawyers who used to earn their livings doing extremely boring document review, but good for everyone else. As in the grocery, book, taxi and hotel businesses, the march of technology is inevitable.

Other advances in law have come with search engines such as Lexmachina, which searches through a small number of databases to predict the outcome of patent cases. Other AI products that have scanned all U.S. Supreme Court decisions do a better job than people in predicting how the court will decide a particular case, based on briefs submitted in a live matter and the judges deciding the case.

When we think about our work gathering facts, we know that most searching is done not in a closed, limited environment. We don’t look through a “mere” four million documents as in a complex discovery or the trivial (for a computer) collection of U.S. Supreme Court cases. Our work is done when the entire world is the possible location of the search.

A person who seldom leaves New York may have a Nevada company with assets in Texas, Bermuda or Russia.

Until all court records in the U.S. are scanned and subject to optical character recognition, artificial intelligence won’t be able to do our job for us in looking over litigation that pertains to a person we are examining.

That day will surely come for U.S records, and may be here in 10 years, but it is not here yet. For the rest of the world, the wait will be longer.

Make no mistake: computers are essential to our business. Still, one set of databases including Westlaw and Lexis Nexis that we often use to begin a case are not as easy to use as Lexmachina or other closed systems, because they rely on abstracts of documents as opposed to the documents themselves.

They are frequently wrong about individual information, mix up different individuals with the same name, and often have outdated material. My profile on one of them, for instance, includes my company but a home phone number I haven’t used in eight years. My current home number is absent. Other databases get my phone number right, but not my company.

Wouldn’t it be nice to have a “Kayak” type system that could compare a person’s profile on five or six paid databases, and then sort out the gold from the garbage?

It would, but it might not happen so soon, and not just because of the open-universe problem.

Even assuming these databases could look to all documents, two other problems arise:

  1. They are on incompatible platforms. Integrating them would be a programming problem.
  2. More importantly, they are paid products, whereas Kayak searches free travel and airline sites. In addition, they require licenses to use, and the amount of data you can get is regulated by one of several permissible uses the user must enter to gain access to the data. A system integration of the sites would mean the integrator would have to vet the user for each system and process payment if it’s a pay-per-use platform.

These are hardly insurmountable problems, but they do help illustrate why, with AI marching relentlessly toward the law firm, certain areas of practice will succumb to more automation faster than others.

What will be insurmountable for AI is this: you cannot ask computers to examine what is not written down, and much of the most interesting information about people resides not on paper but in their minds and the minds of those who know them.

The next installment of this series on AI will consider how AI could still work to help us toward the right people to interview.

A story in today’s Wall Street Journal about “Why the Virtual Reality Hype is About to Come Crashing Down” makes the simple point that computers haven’t caught up to all the permutations of real life to make a “virtual reality” headset experience resemble a genuine experience.

A short demo is one thing, but life goes on after the short demo is finished.

philip segal the art of fact investigation.JPG

“The dirty little secret about [virtual reality] is that the hardware has run ahead of the content,” says the Journal.

My view is that catching up to real life is something that it is hard to see computers doing anytime soon, a point made in my recently published book, The Art of Fact Investigation: Creative Thinking in the Age of Information Overload.

The book makes the case that figuring out problems related to human behavior requires guesswork and the flexibility to change course when one series of guesses appears to be the wrong way forward. Computers are wonderfully flexible and free of emotional bias, but are completely unimaginative.

While computers can sort easily through data people enter onto their hard drives, they have a much harder time saying, “Here is something you should expect to find but do not.” Example: risk management programs failed to note the suspicious fact that Bernard Madoff’s alleged billions under management were audited by a tiny accounting firm in a suburban shopping mall. The computers did not say (because they were not programmed ahead of time to say), “I should be seeing a Big-Four auditor here but I don’t see it.”

But what about all the hype about “Big Data” and our ability to predict things based on billions or individual cases only a computer can keep track of?

The problem is that in some kinds of investigations (who is this particular person? What is the probable reaction of this particular company to litigation?) we don’t demand an answer about what other people or companies have done in the past.

Big data aggregates lots of individual results, but sometimes when the stakes are high, we want to disaggregate and find out what this particular person did at work eight years ago to prompt a departure left off a resume, or what this particular company’s board is like when faced with a lawsuit.

You won’t find those answers in any magical database. If you are lucky and smart, you will find some clues that will help you put together a probable story.

If that sounds less than the neat and tidy solution you were hoping for, who said real life was neat and tidy?

Want to know more?

  • Visit charlesgriffinllc.com and see our two blogs, The Ethical Investigator and the Divorce Asset Hunter;
  • Look at my book, The Art of Fact Investigation (available in free preview for Kindle at Amazon);
  • Watch me speak about Helping Lawyers with Fact Finding, here.