Tag Archives: Artificial Intelligence

AI in eDiscovery: Expectations vs. Reality

AI in eDiscovery

AI in eDiscovery: Expectations vs. Reality

JD Supra Readers Choice Top Author 2020Written by Jim Gill
Content Chief, Ipro

For the last decade, the term Artificial Intelligence (AI) has been used quite a lot in the eDiscovery industry, with many at this point feeling like it’s more hype than technological innovation. This sense of hollow promises was added to recently when a former employee of Hanzo filed a lawsuit claiming that “the company would manually input the investigation results into its customer portal to create the false appearance that they were generated by artificial intelligence,” because the company’s AI platform was not functioning, [while] “senior Hanzo officials repeatedly warned the plaintiff not to tell clients about the manual investigations.”

So if they were faking AI results, does that mean everyone claiming they’re using it in eDiscovery is lying? Not in the least! But it does raise the need to define what is (and isn’t) Artificial Intelligence within eDiscovery as a way to cut through the noise.

Expectations vs. Reality – AI in eDiscovery

There are a lot of claims of “Artificial Intelligence” in the market, but when people hear the term AI, they often think of science-fiction, with robots doing the work that humans had done before. For further insights on the reality of legal AI, Robert Cruz, eDiscovery Hosting Support Consultant at RVM Enterprises, offers some considerations:

“The chatter of the eDiscovery industry lies within the offerings and solutions of ‘AI.’ For most, AI is envisioned from movies like The Terminator, iRobot, Transcendence, Stepford Wives, Blade Runner, and The Matrix. These are illustrations of what true AI would be. But defining AI has been more complex due to companies lowering the bar of what is considered AI.

“In eDiscovery, the industry has been focused on machine learning (predictive coding/technology assisted review), which is a subset of AI. To add to the soup of applications, Natural Language Processing (NLP) and Automated Speech Recognition (ASR) are also in vogue in the industry. However, companies have been selling magic shows with these three applications without illustrating the limitations of the math.

“Nothing will replace the human understanding of language. These 3 applications of AI do not have the capabilities to understand the nuances of human speech dynamics (Morphology, Phonology, Accents, Colloquial terms and Vernacular, Heteronyms). For Technology Assisted Review (TAR), NLP, and ASR, these applications rely on rules to function, and these applications fall short of ‘true AI’ since the rules have to be bent or broken to capture the same meaning between two different languages (which is the role of human translators).

“Heteronyms provide a unique problem when translating. For example: ‘The farm was cultivated to produce produce.’ In my current state of typing, MS Word flagged that sentence as a grammatical error and cannot suggest any corrections!

“So, these current uses of AI in legal (TAR, NLP, ASR) will have greater usage in other areas where automation is governed by rules and speeds up the process for more administrative tasks. For language and speech, we are nowhere near True AI.”

Robert Cruz, eDiscovery Hosting Support Consultant at RVM Enterprises

What is the Future of Legal AI?

The world of technology is constantly changing (for example, it wasn’t so long ago that Netflix was sending DVDs to people through the postal service) so it can be hard to predict what the future may hold for AI. In the legal industry, it can be even more difficult, because, on the one hand you have technological innovation, and on the other, you have whether users will adapt to that innovation.

One of the biggest reasons legal teams may be slow to adapt to the latest breakthroughs, is that, first and foremost, the outcomes have to be defensible. Add this to the fact that AI has primarily been focused on the Review stage of eDiscovery, and it’s easy to see why robot lawyers haven’t swept in and taken over document review.

There’s no doubt that advances will continue in that area (Continuous Active Learning or CAL is already taking its place on the scene), but the future of AI in eDiscovery will likely fall outside of the review stage. Aaron Swenson, Director of Product at Ipro, says it well: “In Legal, you don’t want the AI doing the work for you. You want it to help you ask the right questions, show you insightful trends in the data.”

A good analogy of this is spellcheck and grammar software. It’s been around for quite a while now, but (thankfully) we still need human writers. However, those tools are a great help to a writer. On the other hand, autocorrect (where the computer takes an active role in writing) can be disastrous without human review.

A recent article highlights how AI using word relationships in published scientific papers was able to make discoveries more quickly than humans. A similar use of entity relationships could prove very useful in helping attorneys make connections in large datasets that would be extremely difficult with purely manual review. AI could also help with Assisted Redaction, which could then be QC’d by both software and humans.

The main thing to consider when it comes to Artificial Intelligence within eDiscovery is that it isn’t here to replace lawyers, paralegals, and litigation support personnel. But it can and does and will continue to streamline and assist them in their work of getting to the facts of a case in a just, speedy, and inexpensive manner.

 

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Where Does eDiscovery Fit in the Facial Recognition Conversation?

ediscovery facial recognition

Where Does eDiscovery Fit in the Facial Recognition Conversation?

JD Supra Readers Choice Top Author 2020Written by Jim Gill
Content Chief, Ipro

For most of us, the concept of facial recognition – like so much technology of the last decade – began as a sci-fi detail we accepted on the big screen but didn’t give much thought to in our day-to-day lives. Then one day, our phones started tagging photos automatically, asking, almost sheepishly, “Is this you?” And just like that, the idea that an algorithm could learn to recognize our faces was real. But we’ve moved on from that innocuous beginning and now are treading more and more into the realm of another type of film (I’m thinking here of Brazil or Minority Report) where technology aids law enforcement in making our world a safer place, but also introduces new privacy and ethics conundrums when that technology fails or is corrupted. And, as always, eDiscovery has a place where legal and tech (in this instance law-enforcement and facial recognition) collide.

Law Enforcement, Facial Recognition, and Privacy Concerns

In a Washington Post article in July 2019, it was revealed that agents with the Federal Bureau of Investigation and Immigration and Customs Enforcement were using facial recognition software to scan state driver’s license databases, analyzing millions of Americans’ photos without their knowledge or consent.

In the past, police have used fingerprints, DNA and other “biometric data” collected from criminal suspects (think of those large binders of mugshots you always see victims flipping through in cop shows), but the photos in DMV records are of a state’s residents, most of whom have never been charged with a crime.

According to the Government Accountability Office, the FBI has logged more than 390,000 facial-recognition searches of federal and local databases, including state DMV databases, since 2011. Even though neither Congress nor state legislatures have authorized the development of such a system, and now lawmakers on both sides of the political spectrum are concerned.

“Law enforcement’s access of state databases,” House Oversight Committee Chairman Elijah E. Cummings (D-Md.) said is “often done in the shadows with no consent.” And Rep. Jim Jordan (Ohio), the House Oversight Committee’s ranking Republican, seemed particularly incensed during a hearing into the technology earlier this year.

“They’ve just given access to that to the FBI,” he said. “No individual signed off on that when they renewed their driver’s license, got their driver’s licenses. They didn’t sign any waiver saying, ‘Oh, it’s okay to turn my information, my photo, over to the FBI.’ No elected officials voted for that to happen.”

Off-The-Shelf Facial Recognition Tools for Law Enforcement

In Oregon, back in 2017, the Washington County Sheriff’s Office became the first law enforcement agency in the country known to use Amazon’s artificial-intelligence tool Rekognition, and almost overnight, the deputies of this small county in the suburbs outside of Portland had ramped up their investigative ability. With this off-the-shelf technology, they were able to scan for matches of a suspect’s face across more than 300,000 mug shots taken at the county jail since 2001. With that information, they can take a picture – perhaps captured by a security camera, social-media account, or cellphone – and link it to an identity.

But linking a photo to previous mug shots is analogous to the same action that happened prior to the addition of technology. It’s something detectives used to do manually but now are assisted with the use of AI. Still, there are significant problems with the technology itself.

Facial Recognition Bans Due to Concerns Ranging from Privacy to Racial Equity

Some places have already taken measures to ban face recognition software. In 2019, San Francisco became the first city to ban the technology for law enforcement and government agencies. Similar measures are under consideration in Oakland and Massachusetts. Lawmakers in California are also considering a statewide ban on facial recognition programs.

To add to that list, the largest manufacturer of police body cameras, Axon, is rejecting the possibility of selling facial recognition technology at the recommendation of an independent ethics board which it created last year after acquiring two artificial intelligence companies.

In a 42-page report, the ethics panel found that face recognition technology is not advanced enough for law enforcement to depend on, with concerns ranging from “privacy costs to racial equity.” In fact, the technology was found to be less accurate in identifying the faces of women than men, and younger people compared to older ones. The same was true in people of color, who were harder to correctly identify than white people.

But the lack of regulation or precedent means that, while the technology is being banned in some places, it’s being pursued in others. For instance, Detroit reportedly signed a $1 million deal for software that let it continuously monitor “hundreds of private and public cameras set up around the city,” including gas stations, restaurants, churches and schools, according to the New York Times.

Facial Recognition and eDiscovery

So where does facial recognition fit in eDiscovery? As with any emerging technology, it’s worthwhile for those of us in legaltech to stay abreast of these changes. For one, any new technology is potential ESI that must be discoverable should a criminal or civil case arise in which that electronic data is evidence. Often, people in legal circles might argue, “That hasn’t happened yet, so we’ll worry about it later.” Then again, people a few decades ago might find it unbelievable that data from a phone app (“phone app?” they might add with a puzzled look) meant for requesting rides from a stranger would be used in a nationally covered murder case.

But there is also the notion that facial recognition tools might be used to help attorneys and litigation support specialists do their work. In fact, the company Veritone recently announced an AI eDiscovery tool that makes unstructured data searchable by keywords, faces, and objects.

As technology continues to forge ahead, it’s important that the legal world engages in the conversations surrounding these technologies, before they find themselves deeper in the state of catch up many say they’re already playing.

 

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New Study Shows AI Made Scientific Discoveries Humans Missed:

AI eDiscovery

New Study Shows AI Made Scientific Discoveries Humans Missed: What are the Implications for eDiscovery?

JD Supra Readers Choice Top Author 2020Written by Jim Gill
Content Chief, Ipro

People are always talking about how Artificial Intelligence is the future of legal technology. But up to this point, very few are using it within eDiscovery, and those who are stick with the standard TAR / Predictive Coding approach. Outside of legal, breakthroughs are happening which could open doors for attorneys and investigators to use AI to help sift through large datasets while making connections otherwise not possible with human-only review.

In a recent study published in Nature, researchers from the Lawrence Berkeley National Laboratory used an algorithm called Word2Vec to read scientific papers. The algorithm was given no training in scientific knowledge, instead relying only on word associations. While reviewing over 3 million previously written scientific papers and looking for associations humans may have previously missed, the AI led researchers to knowledge that existed but wasn’t apparent without the help of machine learning.

Vahe Tshitoyan, the lead author on the study, stated, “This algorithm is unsupervised, and it builds its own connections.” Because it’s not trained on a specific dataset, you could easily apply it to other disciplines. He continues, “The information is out there. We just haven’t made these connections yet, because you can’t read every article.”

But any shadow of a doubt can throw a court case into question, which is why bringing AI into the legal sphere is tricky at best. This very well-considered and in-depth article in Law360 discusses why AI Tools need to be litigation ready or “discovery in a lawsuit contesting decisions those tools have made could quickly become a nightmare: Your company may suffer enormous distractions and decreased productivity as it struggles to address litigation requirements that are inconsistent with its AI systems, data and culture; may be subjected to onerous court orders that interfere with its ability to conduct its core businesses; may even suffer adverse judgments on claims that lack merit.”

This highlights the push-pull that exists around AI in the eDiscovery industry: innovators will create technology and find potential uses for it in legal, but the need for data integrity and defensible processes will slow those advances’ practical application.

But forward movement is forward, even if it is slow moving. And in the digital age, even when progress may seem to stall, new approaches can change things seemingly overnight. And even if the Word2Vec algorithm isn’t currently practical for Review, it may be useful during investigations or Early-Case-Assessment, where human reviewers simply need the extra insight AI can give them, allowing them to get to the facts of a case quicker, and then continue through the more standard processes required for defensibility.

Which is why it’s important that we continue to look outside the bubble of legaltech for possibilities. The answers are out there. We just need to make the connections.

 

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Why eDiscovery needs AI

Why eDiscovery needs AI

You can’t talk about eDiscovery without also discussing Artificial Intelligence and its potential impacts on the industry. Some people hear the term and jump to Sci-Fi movies and robots taking over, but it’s much more nuanced than that. You may be wondering how your job will be affected by this new technology. Or maybe you openly embrace technological enhancements. Perhaps you’re in the ‘just ignore it’ camp. You might even have “Skynet becomes self-aware” flashbacks. Whichever bucket you fall in, the fact remains AI is a subject that isn’t going away anytime soon. In many industries, including legal, AI and machine learning is already a thing. While there isn’t a squad of robots running review protocol just yet, smart companies are already leveraging this technology. Here’s a look at just two areas benefited by embracing the AI movement.

eDiscovery:
AI is already hard at work transforming how discovery is done. Gone are the days of attorneys sifting through boxes of documents. Thankfully, that time-consuming process has been replaced by electronic options. As these solutions evolve, we’ve seen the benefits of AI implemented through machine learning capabilities. Slogging through documents looking for that elusive needle in the haystack is now aided by software solutions with features like near-duplicate detection, email threading, and predictive coding. The benefits are obvious- less time spent on the tedious tasks equals more time to spend on meaningful work, which equals cost savings in the long run.

Workforce:
Understandably, the implementation of technology that does the tasks formerly completed by a human worker can cause some uncertainty and anxiety. There are certainly many theories out there about replacing the entire workforce with an army of robots, but that’s just not realistic. At least not anytime soon. There are just some roles, especially in the legal industry, that a human will always be the preferable choice. Let’s look at this common scenario. You’re a seasoned attorney with a large litigation case. The discovery files are in the many terabytes range. Back in the day, you’d have to employ, train, and provide a workspace for who knows how many junior attorneys to complete this task that would likely take many months to finish. You would have to rely on your training and the employee’s competency to locate the required documents needed for the case. There would be challenges- answering questions, employees calling out sick, performance issues. You must manage the project, the case, and the people. Sound familiar?

The solution is not a robot that will replace all the humans, but technology that assists the humans. Instead of eliminating jobs, you’ll be improving job quality by freeing up time from menial, time-consuming tasks for value-added services. The people you have doing review will be more efficient and productive. Rather than digging through documents only to see the same content numerous times, Technology Assisted Review will locate those documents through machine learning features. For the product manager, using technology to assist with identifying and sorting concepts from documents, can arm you with information that will help you make the most of your review time. The project is more efficient, deadlines are met, below budget and everyone is happy. Doesn’t that sound better?

Look, technology won’t go away just by squeezing your eyes closed, so it’s best to be ahead of the game and embrace it. Learning about the direction, benefits and limitations of AI can eliminate a lot of the fear and ambiguity surrounding the topic. Artificial Intelligence is not waiting for us in the future- it’s here. How will you use it?

 

AI in eDiscovery, by Ipro

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