Mind Matters Natural and Artificial Intelligence News and Analysis
Internet law concept
Image licensed via Adobe Stock

You’ve Got a Robot Lawyer in Your Pocket (Really?)

The DoNotPay AI lawyer program might be useful for fighting parking tickets but it is unsuited to serious litigation where much more complex issues are at stake
arroba Email

The Gutfeld! program on Fox News on January 6, 2023, recently had fun discussing robots replacing lawyers to practice law. In faux serious rhyme, Greg Gutfeld intoned: “Can a computer that’s self aware, keep you from the electric chair?” Sparking the conversation was the report that an artificial intelligence (AI) smartphone app was slated to assist a defendant fighting a parking ticket in a currently-undisclosed courtroom:

Gigabytes of text could stream forth addressing the near infinite number of questions raised about robot lawyers. For now, let’s just explore the “robot lawyer” app built by DoNotPay. The company’s website declares: “The DoNotPay app is the home of the world’s first robot lawyer. Fight corporations, beat bureaucracy and sue anyone at the press of a button.”

Really? There are laws against false advertising. Maybe DoNotPay ought to analyze its own potential liability.

“Fight corporations” — means what? “Beat bureaucracy” — 50% of my legal practice involved federal agency disciplinary and employment matters plagued by bureaucracy. What would DoNotPay do for me and my clients that is not happening now?

And “sue anyone”? Try researching and drafting a lawsuit against the Governor of Hawaii for usurping vast powers to control the state using an unconstitutional lockdown during the Covid panic. I wrote the briefs in the Hawaii trial court, appellate court, and supreme court. The litigation proceeded against state attorneys throwing everything they have against the constitutional and statutory law challenges. Would DoNotPay have anything better to offer?

The computer-talented and enthusiastic entrepreneur who developed DoNotPay, Josh Browder, urges: “It’s all about language, and that’s what lawyers charge hundreds or thousands of dollars an hour to do,” and “a lot of lawyers are just charging way too much money to copy and paste documents, and I think they will definitely be replaced, and they should be replaced.”

Browder there honestly shrinks the website’s extravagant claims down to preparing and analyzing written documents — not litigation of complex matters. It’s a common misconception that lawyers spend a lot of time cutting and pasting documents, charging hundreds or thousands of dollars to do it. Sure, some of that does occur, and some legal work is just preparing routine fill-in-the-blank documents. However, paralegals with computers have reduced those costs enormously.

Looking clearly at the issues that litigation addresses, the DoNotPay app helps people fight unjust parking tickets — a noble goal perhaps — by stepping through a series of questions that the user answers. While in college, I represented myself in a parking ticket trial, my first case ever, my first cross-examination ever, and won. How? By reading the California statute, stepping through the terms one by one, and finding the element of the case the prosecution could not overcome.

That kind of assistance DoNotPay can reasonably provide: identify the elements of the crime, for example, and assemble user input to figure out if the crime can be proved, or what facts might provide legal defenses:

But suing the Governor of Hawaii or a federal agency – not a likely DoNotPay skill set.

What about Browder’s claim that practicing law is “all about language” and massaging language is about all lawyers do? False.

In a nutshell, a litigation lawyer’s job starts with information gathering: hearing and understanding clients’ statements and documents in the human context where some other person has done something harmful or takes an opposing view of a conflict. Next, the lawyer looks for laws and legal doctrines that can support a claim or defend against it — called issue spotting — a good lawyer looks at both the claims and the defenses in every situation.

Early in the process, the lawyer thinks about persuasion: whether the client’s facts will persuade a judge or jury. The lawyer develops a litigation plan to prove the client’s position if possible, and develop more evidence, whether helpful or harmful to the client’s position, so the entire case can be evaluated.

In some cases, the lawyer may attempt to negotiate a compromise or settlement with the opposing party. Settlement is an art combining knowledge of facts and law, likelihoods of persuading a judge or jury, the interests and goals (which may be emotion-based) of both the client and the opposing party, and bargaining methods to reach agreement — with sometimes very stressed and unhappy human beings.

Cases that do not settle may be taken to trial or arbitration, where lawyers deploy a broad range of skills, many of them subjective, many of them judgment calls made as new factors arise in real time. This description hasn’t even touched on all the pre-trial, trial, and post-trial motions that are written and opposed in all but the simplest litigation cases.

For now, consider one other thought. The Gutfeld! episode suggested that automating legal practice is akin to computerizing the reading of x-rays, which in fact expands to analyzing EKGs, EEGs, and medical test results. Reading x-rays is a confined task, one that looks at a snapshot of fixed data that depends little upon human context. Neural network AI canvasses tens of thousands of x-rays along with the human interpretation data for each one, finding patterns and relationships that can be generalized. As wonderful as that AI power is, it succeeds because it has “if-then” certainties or probabilities on which it can build.

Diagnosing everything from Covid to lung cancer depends upon identifying objective and subjective factors and then comparing them to previous knowledge and experience with the same factors. But, unlike medical data where two patients can have near identical signs, symptoms, syndromes, and lab results, litigation law has few “if-then” elements and no two cases are alike.

For example, when a neighbor’s child is bitten by another family’s pit bull for the first time, without warning, what can a neural network search of dog bite cases offer? Prior cases don’t explain what happened in the current case. Yet, what happened in the current case means everything when deciding liability and damages. Was the dog provoked, ill, or just randomly violent? Did the dog’s owner act unreasonably, or recklessly, or intentionally, in a way that made the dog a danger to people? How should the damages to the child be calculated, beyond medical bills and including pain, suffering, disfigurement, long-term disability? All of these questions make a “simple dog bite case” not readily computed by a machine that has information about other dog bite cases.

To answer Greg Gutfeld’s playful verse: No — don’t trust a computer, because it is not in fact “self-aware” — you’ll need a lawyer to help you avoid the electric chair.

Richard Stevens

Fellow, Walter Bradley Center on Natural and Artificial Intelligence
Richard W. Stevens is a lawyer, author, and a Fellow of Discovery Institute's Walter Bradley Center on Natural and Artificial Intelligence. He has written extensively on how code and software systems evidence intelligent design in biological systems. He holds a J.D. with high honors from the University of San Diego Law School and a computer science degree from UC San Diego. Richard has practiced civil and administrative law litigation in California and Washington D.C., taught legal research and writing at George Washington University and George Mason University law schools, and now specializes in writing dispositive motion and appellate briefs. He has authored or co-authored four books, and has written numerous articles and spoken on subjects including legal writing, economics, the Bill of Rights and Christian apologetics. His fifth book, Investigation Defense, is forthcoming.

You’ve Got a Robot Lawyer in Your Pocket (Really?)