Artificial intelligence

Artificial intelligence doesn’t equal artificial perfection. I have argued for a while now both on this blog and in a forthcoming law review article here that lawyers (and the investigators who work for them) have little to fear and much to gain as artificial intelligence gets smarter.

Computers may be able to do a lot more than they used to, but there is so much more information for them to sort through that humans will long be required to pick through the results just as they are now. Right now, we have no quick way to word-search the billions of hours of YouTube videos and podcasts, but that time is coming soon.

The key point is that some AI programs will work better than others, but even the best ones will make mistakes or will only get us so far.

So argues British math professor Hannah Fry in a new book previewed in her recent essay in The Wall Street Journal, here. Fry argues that instead of having blind faith in algorithms and artificial intelligence, the best applications are the ones that we admit work somewhat well but are not perfect, and that require collaboration with human beings.

That’s collaboration, not simply implementation. Who has not been infuriated at the hands of some company, only to complain and be told, “that’s what the computer’s telling me.”

The fault may be less with the computer program than with the dumb company that doesn’t empower its people to work with and override computers that make mistakes at the expense of their customers.

Fry writes that some algorithms do great things – diagnose cancer, catch serial killers and avoid plane crashes. But, beware the modern snake-oil salesman:

Despite a lack of scientific evidence to support such claims, companies are selling algorithms to police forces and governments that can supposedly ‘predict’ whether someone is a terrorist, or a pedophile based on his or her facial characteristics alone. Others insist their algorithms can suggest a change to a single line of a screenplay that will make the movie more profitable at the box office. Matchmaking services insist their algorithm will locate your one true love.

As importantly for lawyers worried about losing their jobs, think about the successful AI applications above. Are we worried that oncologists, homicide detectives and air traffic controllers are endangered occupations? Until there is a cure for cancer, we are not.

We just think these people will be able to do their jobs better with the help of AI.

An entire day at a conference on artificial intelligence and the law last week in Chicago produced this insight about how lawyers are dealing with the fast-changing world of artificial intelligence:

Many lawyers are like someone who knows he needs to buy a car but knows nothing about cars. He knows he needs to get from A to B each day and wants to get there faster. So, he is deposited at the largest auto show in the world and told, “Decide which car you should buy.”

Whether it’s at smaller conferences or at the gigantic, auto-show-like legal tech jamborees in Las Vegas or New York, the discussion of AI seems to be dominated by the companies that produce the stuff. Much less on show are people who use legal AI in their everyday lives.

At my conference, the keynote address (and two more panels) were dominated by IBM. Other familiar names in AI in the world of smart contracting and legal research were there, along with the one of the major “old tech” legal research giants. All of the products and services sounded great, which means the salespeople were doing their jobs.

But the number of people who presented about actually using AI after buying it? Just a few (including me). “We wanted to get more users,” said one of the conference organizers, who explained that lawyers are reluctant to describe the ways they use AI, lest they give up valuable pointers to their competitors.

Most of the questions and discussion from lawyers centered around two main themes:

  1. How can we decide which product to buy when there are so many, and they change so quickly?
  2. How can we organize our firm’s business model in such a way that it will be profitable to use expensive new software (“software” being what AI gets called after you start using it)?

Law firm business models are not my specialty, but I have written before and spoke last week about evaluating new programs.

Only you (and not the vendor) can decide how useful a program is, by testing it. Don’t let the vendors feed you canned examples of how great their program is. Don’t put in a search term or two while standing at a trade show kiosk. Instead, plug in a current problem or three while sitting in your office and see how well the program does compared to the searches you ran last week.

You mean you didn’t run the searches, but you’re deciding whether to buy this expensive package? You should at least ask the people who will do the work what they think of the offering.

I always like to put in my own company or my own name and see how accurate a fact-finding program is. Some of them (which are still useful some of the time) think I live in the house I sold eight years ago. If you’re going to buy, you should know what a program can do and what it can’t.

As with other salespeople in other industries, AI sales staff won’t tell you what their programs are bad at doing. And most importantly, they won’t tell you how well or how badly (usually badly) their program integrates with other AI software you may be using.

No matter how good any software is, you will need good, inquisitive and flexible people running it and helping to coordinate outputs of different products you are using.

While sales staff may have subject-matter expertise in law (it helps if they are lawyers themselves) they cannot possibly specialize in all facets of the law. Their job is to sell, and they should not be criticized for it.

They have their job to do, and as a responsible buyer, you have yours.

For more on what an AI testing program could look like and what kinds of traits the best users of AI should have, see my forthcoming law review article here:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3085263

 

Do you ever wonder why some gifted small children play Mozart, but you never see any child prodigy lawyers who can draft a complicated will?

The reason is that the rules of how to play the piano have far fewer permutations and judgment calls than deciding what should go into a will. “Do this, not that” works well with a limited number of keys in each octave. But the permutations of a will are infinite. And by the way, child prodigies can play the notes, but usually not as soulfully as an older pianist with more experience of the range of emotions an adult experiences over a lifetime.

You get to be good at something by doing a lot of it. You can play the Mozart over and over, but how do you know what other human beings may need in a will, covering events that have yet to happen?

Not by drafting the same kind of will over and over, that’s for sure.

Reviewing a lot of translations done by people is the way Google Translate can manage rudimentary translations in a split second. Reviewing a thousand decisions made in document discovery and learning from mistakes picked out by a person is the way e-discovery software looks smarter the longer you use it.

But you would never translate a complex, nuanced document with Google Translate, and you sure wouldn’t produce documents without having a partner look it all over.

The craziness that can result from the mindless following of rules is an issue on the forefront law today, as we debate how much we should rely on artificial intelligence.

Who should bear the cost if AI makes a decision that damages a client? The designers of the software? The lawyers who use it? Or will malpractice insurance evolve enough to spread the risk around so that clients pay in advance in the form of a slightly higher price to offset the premium paid by the lawyer?

Whatever we decide, my view is that human oversite of computer activity is something society will need far into the future. The Mozart line above was given to me by my property professor in law school and appeared in the preface of my book, The Art of Fact Investigation.

The Mozart line is appropriate when thinking about computers, too. And in visual art, I increasingly see parallels between the way artists and lawyers struggle to get at what is true and what is an outcome we find desirable. Take the recent exhibition at the Metropolitan Museum here in New York, called Delirious: Art at the Limits of Reason, 1950 to 1980.

It showed that our struggle with machines is hardly new, even though it would seem so with the flood of scary stories about AI and “The Singularity” that we get daily. The show was filled with the worrying of artists 50 and 60 years ago about what machines would do to the way we see the world, find facts, and how we remain rational. It seems funny to say that: computers seem to be ultra-rational in their production of purely logical “thinking.”

But what seems to be a sensible or logical premise doesn’t mean that you’ll end up with logical conclusions. On a very early AI level, consider the databases we use today that were the wonders of the world 20 years ago. LexisNexis or Westlaw are hugely powerful tools, but what if you don’t supervise them? If I put my name into Westlaw, it thinks I still live in the home I sold in 2011. All other reasoning Westlaw produces based on that “fact” will be wrong. Noise complaints brought against the residents there have nothing to do with me. A newspaper story about disorderly conduct resulting in many police visits to the home two years ago are also irrelevant when talking about me.[1]

The idea of suppositions running amok came home when I looked at a sculpture last month by Sol LeWitt (1928-2007) called 13/3. At first glance, this sculpture would seem to have little relationship to delirium. It sounds from the outset like a simple idea: a 13×13 grid from which three towers arise. What you get when it’s logically put into action is a disorienting building that few would want to occupy.

As the curators commented, LeWitt “did not consider his otherwise systematic work rational. Indeed, he aimed to ‘break out of the whole idea of rationality.’ ‘In a logical sequence,’ LeWitt wrote, in which a predetermined algorithm, not the artist, dictates the work of art, ‘you don’t think about it. It is a way of not thinking. It is irrational.’”

Another wonderful work in the show, Howardena Pindell’s Untitled #2, makes fun of the faith we sometimes have in what superficially looks to be the product of machine-driven logic. A vast array of numbered dots sits uneasily atop a grid, and at first, the dots appear to be the product of an algorithm. In the end, they “amount to nothing but diagrammatic babble.”

Setting a formula in motion is not deep thinking. The thinking comes in deciding whether the vast amount of information we’re processing results in something we like, want or need. Lawyers would do well to remember that.

[1] Imaginary stuff: while Westlaw does say I live there, the problems at the home are made up for illustrative purposes.

We’ve had a great response to an Above the Law op-ed here that outlined the kinds of skills lawyers will need as artificial intelligence increases its foothold in law firms.

The piece makes clear that without the right kinds of skills, many of the benefits of AI will be lost on law firms because you still need an engaged human brain to ask the computer the right questions and to analyze the results.

But too much passivity in the use of AI is not only inefficient. It also carries the risk of ethical violations. Once you deploy anything in the aid of a client, New York legal ethics guru Roy Simon says you need to ask,

“Has your firm designated a person (whether lawyer or nonlawyer) to vet, test or evaluate the AI products (and technology products generally) before using them to serve clients?”

We’ve written before about ABA Model Rule 5.3 that requires lawyers to supervise the investigators they hire (and “supervise” means more than saying “don’t break any rules” and then waiting for the results to roll in). See The Weinstein Saga: Now Featuring Lying Investigators, Duplicitous Journalists, Sloppy Lawyers.

But Rule 5.3 also pertains to supervising your IT department. It’s not enough to have some sales person convince you to buy new software (AI gets called software once we start using it). The lawyer or the firm paying for it should do more than rely on claims by the vendor.

Simon told a recent conference that you don’t have to understand the code or algorithms behind the product (just as you don’t have to know every feature of Word or Excel), but you do need to know what the limits of the product are and what can go wrong (especially how to protect confidential information).

In addition to leaking information it shouldn’t, what kinds of things are there to learn about how a program works that could have an impact on the quality of the work you do with it?

  • AI can be biased: Software works based on the assumptions of those who program it. You can never get a read in advance of what a program’s biases may do to output until you use the program. Far more advanced than the old saying “garbage in-garbage out,” but a related concept: there are thousands of decisions a computer needs to make based on definitions a person inserts either before the thing comes out of the box or during the machine-learning process where people refine results with new, corrective inputs.
  • Competing AI programs can do some things better than others. Which programs are best for Task X and which for Task Y? No salesperson will give you the complete answer. You learn by trying.
  • Control group testing can be very valuable. Ask someone at your firm to do a search for which you know the results and see how easy it is for them to come up with the results you know you should see. If the results they come up with are wrong, you may have a problem with the person, with the program, or both.

The person who should not be leading this portion the training is the sales representative of the software vendor. Someone competent at the law firm needs to do it, and if they are not a lawyer then a lawyer needs to be up on what’s happening.

[For more on our thoughts on AI, see the draft of my paper for the Savannah Law Review, Legal Jobs in the Age of Artificial Intelligence: Moving from Today’s Limited Universe of Data Toward the Great Beyond, available here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3085263].

 

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.

By now, if a lawyer isn’t thinking hard about how automation is going transform the business of law, that lawyer is a laggard.

You see the way computers upended the taxi, hotel, book and shopping mall businesses? It’s already started in law too.  As firms face resistance over pricing and are looking to get more efficient, the time is now to start training people to work with – and not in fear of – artificial intelligence.

And be not afraid.
And be not afraid

There will still be plenty of lawyers around in 10 or 20 years no matter how much artificial intelligence gets deployed in the law. But the roles those people will play will in many respects be different. The new roles will need different skills.

In a new Harvard Business Review article (based on his new book, “Humility is the New Smart”) Professor Ed Hess at the Darden School of Business argues that in the age of artificial intelligence, being smart won’t mean the same thing as it does today.

Because smart machines can process, store and recall information faster than any person, the skills of memorizing and recall are not as important as they once were. The new smart “will be determined not by what or how you know but by the quality of your thinking, listening, relating, collaborating and learning,” Hess writes.

Among the many concrete things this will mean for lawyers are two aspects of fact investigation we know well and have been writing about for a long time.

  1. Open-mindedness will be indispensable.
  2. Even for legal research, logical deduction is out, logical inference is in.

Hess predicts we will “spend more time training to be open-minded and learning to update our beliefs in response to new data.” What could this mean in practice for a lawyer?

If all you know how to do is to gather raw information from a limited universe of documents, or perhaps spend a lot of time cutting and pasting phrases from old documents onto new ones, your days are numbered. Technology-assisted review (TAR) already does a good job sorting out duplicates and constructing a chain of emails so you don’t have to read the same email 27 times as you read through a long exchange.

But as computers become smarter and faster, they are sometimes overwhelmed by the vast amounts of new data coming online all the time. I wrote about this in my book, “The Art of Fact Investigation: Creative Thinking in the Age of Information Overload.”

I made the overload point with respect to finding facts outside discovery, but the same phenomenon is hitting legal research too.

In their article “On the Concept of Relevance in Legal Information Retrieval” in the Artificial Intelligence and Law Journal earlier this year,[1] Marc van Opijnen and Cristiana Santos wrote that

“The number of legal documents published online is growing exponentially, but accessibility and searchability have not kept pace with this growth rate. Poorly written or relatively unimportant court decisions are available at the click of the mouse, exposing the comforting myth that all results with the same juristic status are equal. An overload of information (particularly if of low-quality) carries the risk of undermining knowledge acquisition possibilities and even access to justice.

If legal research suffers from the overload problem, even e-discovery faces it despite TAR and whatever technology succeeds TAR (and something will). Whole areas of data are now searchable and discoverable when once they were not. The more you can search, the more there is to search. A lot of what comes back is garbage.

Lawyers who will succeed in using ever more sophisticated computer programs will need to remain open-minded that they (the lawyers) and not the computers are in charge. Open-minded here means accepting that computers are great at some things, but that for a great many years an alert mind will be able to sort through results in a way a computer won’t. The kind of person who will succeed at this will be entirely active – and not passive – while using the technology. Anyone using TAR knows that it requires training before it can be used correctly.

One reason the mind needs to stay engaged is that not all legal reasoning is deductive, and logical deduction is the basis for computational logic. Michael Genesereth of Codex, Stanford’s Center for Legal Informatics wrote two years ago that computational law “simply cannot be applied in cases requiring analogical or inductive reasoning,” though if there are enough judicial rulings interpreting a regulation the computers could muddle through.

For logical deduction to work, you need to know what step one is before you proceed to step two.  Sherlock Holmes always knew where to start because he was the character in entertaining stories of fiction. In solving the puzzle he laid it out in a way that seemed as if it was the only logical solution.

But it wasn’t. In real life, law enforcement, investigators and attorneys faced with mountains of jumbled facts have to pick their ways through all kinds of evidence that produces often mutually contradictory theories. The universe of possible starting points is almost infinite.

It can be a humbling experience to sit in front of a powerful computer armed with great software and connected to the rest of the world, and to have no idea where to begin looking, when you’ve searched enough, and how confident to be in your findings.

“The new smart,” says Hess, “will be about trying to overcome the two big inhibitors of critical thinking and team collaboration: our ego and our fears.”

Want to know more about our firm?

  • Visit charlesgriffinllc.com and see our two blogs, this one and The Divorce Asset Hunter;
  • Look at my book, The Art of Fact Investigation (available in free preview for Kindle at Amazon). There is a detailed section on logic and inference in the law.
  • Watch me speak about Helping Lawyers with Fact Finding, here. We offer training for lawyers, and I speak across the country to legal groups about the proper mindset for legal inquiry.
  • If you are member of the ABA’s Litigation Section, see my piece in the current issue of Litigation Journal, “Five Questions Litigators Should Ask: Before Hiring an Investigator (and Five Tips to Investigate It Yourself). It contains a discussion of open-mindedness.

[1] van Opijnen, M. & Santos, C. Artif Intell Law (2017) 25: 65. doi:10.1007/s10506-017-9195-8