Recently, we posted a shallow dive into eDiscovery and what it entailed.
In light of the global pandemic, the sudden world shift into virtual environments, lockdowns, and working from home, eDiscovery has been gaining traction in the legal landscape. Not only does eDiscovery allow for easy access to ESI (Electronically Stored Information), but we dive into how artificial Intelligence is used to propel the technology.
eDiscovery that harnesses the capabilities and powers of AI is called Intelligent eDiscovery. It provides virtual tools for lawyers & legal professionals to use when reviewing discovery documents. There are two main types of AI that the field of eDiscovery is beginning to use widely:
- Natural Language Processing (NLP)
- Machine Learning (ML)
Both of these AI applications will shape the future of Intelligent eDiscovery.
NLP allows for computers to communicate in the same language as us. It is not a new technology, and have been improved by machine learning and years of AI research over the past decade. Natural Language processing is often used in Big Data Research in Unstructured Data Anaysis, and its role is to enable the computer to understand written and spoken human language and search for patterns.
Firstly, NLP is able to sift through all discovery documents and categorize them so that it is easier to manage. NLP is also able to pick up on patterns of speech and pick up on things that a human, reading through thousands of documents, may miss.
For instance, NLP is able spot red flags in patterns of people’s text or speech. An example is if someone is being very talkative in an email, text, etc.and they suddenly ask for the other party to call them on the phone. NLP will flag that action as it goes against the identified pattern of speech/text. The automation of tedious tasks such as these have propelled the efficiency and accuracy of legal professionals significantly.
Working in tandem with NLP is Machine Learning (ML), an AI that is able to learn as it collects & analyses more and more data. In eDiscovery, ML is used for Technology-Assisted Review or TAR.
TAR uses NLP for document analysis. An attorney can insert all discovery documents into TAR, and it will return which files are relevant or irrelevant, essentially how search engines show you what websites or links are most relevant to what you have searched. TAR then analyzes all of the documents using NLP and begins to learn and identify patterns, tendencies, and other recurring words used by the counterparty.
TAR also learns what the attorney is looking for as they look at and extract specific documents. In one study, TAR was able to identify 95% of relevant information while humans were only able to identify 51%.
In short, ML or TAR is a great tool for identifying relevant information and reporting it to the attorney. Intelligent eDiscovery is the next step from current forms of eDiscovery, and it is only making it easier for attorneys to be more effective and efficient in their discovery proceedings, directly impacting their success as an attorney for their clients.
As NLP and ML continue to become more enhanced, it is exciting to think about the possibilities than AI can bring about in the future of eDiscovery.
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