Skip to main content

New ‘A.I. lawyer’ analyzes your emails to find moneysaving loopholes

Email systems have gotten smarter. Whether it’s filtering out spam, prioritizing the messages we need to respond to, reminding us when we’ve forgotten to include a mentioned attachment, or suggesting appropriate responses, 2020 email has come a long way from the basic inboxes of yesteryear. But there’s still further they can go — and Joshua Browder, the creator of the robot lawyer service DoNotPay, believes he’s come up with a way to make email even more user-friendly. (Hint: It involves saving people money.)

Browder, for those unfamiliar with him, is the legal tech genius who has been creating automated legal bots for the past several years. Whether it’s helping appeal parking fines (where the original DoNotPay name came from) or aiding people in gaining unemployment benefits, he’s focused on one consumer rights area after the other to disrupt through automation.

DoNotPay Email, which launched Wednesday, gives users a new @DoNotPay email account that automatically analyzes every incoming message and identifies ways to save you time, money, or both. Let’s say that you get an email from your gym, for instance. Rather than just determining it to be spam or non-spam, as a conventional email system might do, DoNotPay Email will chime in with the option of letting you cancel your membership with a single click should you wish to do that. Similarly, an email receipt about a message concerning poor in-flight Wi-Fi will cause the system to automatically fight the airline for your refund. Or an obvious bit of spam will not only let you “unsubscribe,” but also search for any pending class action suits involving the sender and help you claim cash compensation for being bothered. It works by matching the contents of each email to the 150 existing consumer rights products the company already offers.

“We have built a machine model based on five years of DoNotPay data to successfully classify the emails,” Browder, whose work we have profiled in-depth before, told Digital Trends. “As a result, it will work with any [message] in the correct category. This technical approach has been our long term vision for a while. Currently, most of our users come to DoNotPay with a problem. But in the future, we want ML to be able to act on peoples’ behalf automatically and ‘push’ savings to people that they didn’t even know about.”

Contrary to its name, DoNotPay Email isn’t actually free. It costs $3 per month as a rolling subscription, although Browder said that this means his organization is not “beholden to the companies that we are fighting against.” Just how well a robot lawyer in your inbox works remains to be seen, but DoNotPay certainly has a history in this area. According to the company, DoNotPay passed the 1 million cases solved milestone this June, saving customers more than $30 million in the process.

Editors' Recommendations

Luke Dormehl
I'm a UK-based tech writer covering Cool Tech at Digital Trends. I've also written for Fast Company, Wired, the Guardian…
A.I. fail as robot TV camera follows bald head instead of soccer ball
ai fail as robot camera mistakes bald head for soccer ball

CaleyJags : SPFL Championship : Real Highlights: ICTFC 1 v 1 AYR : 24/10/2020

While artificial intelligence (A.I.) has clearly made astonishing strides in recent years, the technology is still susceptible to the occasional fail.

Read more
This basic human skill is the next major milestone for A.I.
profile of head on computer chip artificial intelligence

Remember the amazing, revelatory feeling when you first discovered the existence of cause and effect? That’s a trick question. Kids start learning the principle of causality from as early as eight months old, helping them to make rudimentary inferences about the world around them. But most of us don’t remember much before the age of around three or four, so the important lesson of “why” is something we simply take for granted.

It’s not only a crucial lesson for humans to learn, but also one that today’s artificial intelligence systems are pretty darn bad at. While modern A.I. is capable of beating human players at Go and driving cars on busy streets, this is not necessarily comparable with the kind of intelligence humans might use to master these abilities. That’s because humans -- even small infants -- possess the ability to generalize by applying knowledge from one domain to another. For A.I. to live up to its potential, this is something it also needs to be able to do.

Read more
This groundbreaking new style of A.I. learns things in a totally different way
History of AI neural networks

With very rare exceptions, every major advance in artificial intelligence this century has been the result of machine learning. As its name implies (and counter to the symbolic A.I. that characterized much of the first half of the field’s history), machine learning involves smart systems that don’t just follow rules but actually, well, learn.

But there’s a problem. Unlike even a small human child, machine learning needs to be shown large numbers of training examples before it can successfully recognize them. There’s no such thing as, say, seeing an object like a “doofer” (you don’t know what it is, but we bet you would remember it if you saw one) and, thereafter, being able to recognize every subsequent doofer you see.

Read more