Is AI Replacing Lawyers and their Staff?
By Mark G. Walker, VP Advisory Services and Susan Kavanagh, Senior Advisor -iControl ESIIntroduction
Don’t shoot the messenger! You can’t make this stuff up. Or can you? There have been several articles recently suggesting that Artificial Intelligence (AI) is creeping into the legal arena and that “Lawyers could be the next profession to be replaced by computers”. That’s literally the title of a recent CNBC “Future of Work” article.
“The legal profession — tradition-bound and labor-heavy — is on the cusp of a transformation in which artificial-intelligence platforms dramatically affect how legal work gets done.”
To be fair, despite the catchy title, that article - like this one - didn’t conclude that AI was a threat to the legal profession in that it would eliminate jobs. In fact, the opposite is true. "I think we will see a rise of more jobs in the legal market as a result of AI", said Andrew Arruda, CEO of Ross Intelligence.
Sofia Lingos, a lawyer and board member of the Legal Tech Resource Center of the American Bar Association, when asked if lawyers should be afraid or encouraged by AI said:
“Both. It is wise to embrace it now so that it can be a tool as opposed to an impediment. No one wants to be competing against Watson," Lingos said, referring to IBM's cognitive computer system.
"But if you can't beat 'em, join 'em!"The Future Work article discusses more than just using machine learning (a sub science of AI) to reduce the number of documents needing human review. The article also explores other areas where AI is beginning to creep into traditional corporate legal work such as contracts, wills, real-estate and general, repetitive legal work.. AI allows legal teams to do more interesting work they were trained to do and “automate” much of the drudge work. Lawyers didn’t necessarily go to law school to spend their first 5 years in document reviews, so you would think that the legal community would be flocking to use this technology. This however, has not been the case.
To understand how AI is indeed changing how we manage ESI specifically, we need to understand the context.
Image Credit: TheMerkie.com
AI and Document Review
So why aren’t more lawyers adopting this game changing technology? Truthfully, the burden of explaining how machine learning works rests squarely on the shoulders of services providers who create, deliver and operate this technology. Too often the discussion has been lost in endless technical debates about whose product is the best on the market. What follows is our attempt at an intentionally oversimplified explanation of how machine learning functions in document review.
Five Stage Process
AI is not going to replace lawyers anytime soon. In fact, think of the technology in the context of Electronically Stored Information (ESI) as a sophisticated search and find tool. How technology performs is much more sophisticated in “classifying” and retrieving information than a simple search or near dupe identification. A good sales approach by a service provider can convince any end-user that their technology is the best. However, in their simplest form, all technology is reading words and phrases, and classifying documents by creating unique conceptional buckets and then placing related documents into those conceptual buckets. Humans then teach the technology which buckets are relevant or not relevant by reviewing samples presented to them by the technology. For a more detailed 5 step process involving search term validation, see our five part series –Combining Predictive Coding and Search Term Classification in 5 Easy Step.
Stage 1 – Judgmental Seeds
A typical document collection usually contains a very low percentage of relevant documents. Usually between 1 - 5 out of every 100 documents reviewed will be relevant. We are, in most cases, looking for the needle in a haystack. So, the first step in teaching the machine what is relevant is to increase the odds that relevant examples will be selected for consideration. We provide the technology with "seeds". These seeds can be examples of known relevant and not relevant documents. Or, more commonly provide a list of search terms that will help find a larger number of relevant documents - roughly 20 or 30 out of 100. Key terms do not have to be tested, and if search terms are being exchanged with the opposition, the technology will help validate those terms by deploying our 5 Step Processes referenced above.
Stage 2 - Training and Uncertainty Sampling
Stage 2 - Training and Uncertainty Sampling
Reviewers tag each document in the seed set as relevant or not relevant (if not already known). It is really that simple. Lawyers will then review documents the traditional way they have always conducted document review.
Stage 3 – Predictive Analysis
The algorithms within the software analyze the human decisions from Stage 1 and offers predictions based on comparing the content from the seed documents to other documents with similar content. The software will then classify each document available into one of three buckets:
The algorithms within the software analyze the human decisions from Stage 1 and offers predictions based on comparing the content from the seed documents to other documents with similar content. The software will then classify each document available into one of three buckets:
- Potentially Relevant
- Potentially Not Relevant
- Uncertain - reviewers did not review any samples like these
Stage 4 – Sample, Train and Stabilize
If you’re using active learning technology, the software will feed examples of documents it thinks are relevant, not relevant and “uncertain” documents to the reviewer. The uncertainty sampling is critical. With software that uses only passive learning there are many more steps that need to take place. Therefore, before you engage machine learning, make sure you know whether you have active learning available to you. With passive learning workflows, while the end result will be the same, it will take much longer to get there.
As the iterative review of training samples continues, the software should provide stabilization indicators that allow those administering the review the ability to determine when the machine learning has stabilized. Different software packages measure stabilization differently, so it is important to establish your stabilization goal. For example, iControl ESI’s ENVIZE solution, has its own stabilization indicators that tell us how often the human agrees or disagrees with the software predictions as the primary, but not exclusive indicator, of when stabilization has occurred and the software is not gaining anymore knowledge about what is relevant.
Stage 5 – Complete Review
Once -stabilization has occurred and the software has a good idea what is or is not relevant, you can then proceed with review. With some software, you can continuously review only the most highly ranked relevant documents at any stage in the process. This is often referred to as “Continuous Active Learning” or CAL.
Conclusion
Predictive Coding is growing rapidly. While it indeed uses machine learning, which is a subset of AI science, we are not deploying sentient computerized beings that think for themselves. The AI we use today to reduce document review does not decide what is relevant on its own. The technology must be taught how to perform correctly. That said, it is entirely possible that perhaps one day Watson will replace living, breathing and thinking lawyers, but that is the reality of today. At present, the technology is continuing to mature and the subset of AI we prefer to refer to as Machine Learning, is the future, and it is a very powerful tool to lawyers and legal staff.
Tags: #machinelearning, #predictiveanalytics, #ediscovery , #predictivecoding, #bigdata, #AI
If you’re using active learning technology, the software will feed examples of documents it thinks are relevant, not relevant and “uncertain” documents to the reviewer. The uncertainty sampling is critical. With software that uses only passive learning there are many more steps that need to take place. Therefore, before you engage machine learning, make sure you know whether you have active learning available to you. With passive learning workflows, while the end result will be the same, it will take much longer to get there.
As the iterative review of training samples continues, the software should provide stabilization indicators that allow those administering the review the ability to determine when the machine learning has stabilized. Different software packages measure stabilization differently, so it is important to establish your stabilization goal. For example, iControl ESI’s ENVIZE solution, has its own stabilization indicators that tell us how often the human agrees or disagrees with the software predictions as the primary, but not exclusive indicator, of when stabilization has occurred and the software is not gaining anymore knowledge about what is relevant.
Stage 5 – Complete Review
Once -stabilization has occurred and the software has a good idea what is or is not relevant, you can then proceed with review. With some software, you can continuously review only the most highly ranked relevant documents at any stage in the process. This is often referred to as “Continuous Active Learning” or CAL.
Conclusion
Predictive Coding is growing rapidly. While it indeed uses machine learning, which is a subset of AI science, we are not deploying sentient computerized beings that think for themselves. The AI we use today to reduce document review does not decide what is relevant on its own. The technology must be taught how to perform correctly. That said, it is entirely possible that perhaps one day Watson will replace living, breathing and thinking lawyers, but that is the reality of today. At present, the technology is continuing to mature and the subset of AI we prefer to refer to as Machine Learning, is the future, and it is a very powerful tool to lawyers and legal staff.
Tags: #machinelearning, #predictiveanalytics, #ediscovery , #predictivecoding, #bigdata, #AI
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