e-Discovery is set to grow tremendously in the future. Statistics predict growth from almost 11 billion dollars in 2020 to 15 billion dollars in 2025 in its software and services, primarily fuelled by investments in legal technology.
But what does the term mean? It’s a process of identifying, collecting, and producing data required for an investigation or legal case to be used as evidence. It includes emails, images, websites, instant messaging chats, and databases.
However, the transformation of ediscovery ai has impressed everybody involved in this field, making the process faster and more efficient while reducing potential errors.
Because electronically stored data (ESI) is massive as it’s stored across computers, smartphones, desktops, file shares, backup systems, and accounting systems, it can be challenging for the teams involved to identify relevant documents and files for a particular case.
Machine learning helps humans better understand the data, trace the missing links between crucial pieces of information, and even improve the review management process.
But those are not the only benefits. What are some others that are worth knowing? Read on to find out.
How Does AI Work in e-Discovery?
Two types of artificial intelligence have primarily impacted this field: machine learning and natural language processing (NLP). Machine learning takes the help of mathematical models to examine and study massive datasets and get results.
Natural process learning refers to AI’s capability to understand written and spoken human language and cognitive patterns. Technology has advanced to the degree where things called “sentimental analysis” have become possible.
The sentimental analysis goes a step further than the usual data analysis. It studies the users’ facial expressions and voice recordings concerning particular sentiments.
Benefits of AI in e-Discovery
Speeds up the process
The legal industry runs based on billable hours, and speed is of the utmost importance. AI enables legal teams to extract the cast of characters or communication strands that provide maximum informationabout a case, which legal teams can use to prepare review strategies.
Identifying new documents
AI is efficient at saving precious time, as mentioned above. It helps teams find new documents using standard search terms and analytics rather than the usual ones. It can do this by browsing the previously searched documents that pertain to a particular case.
Searching for irregularities
Machine learning can also search for anomalies or irregularities that aren’t traceable by humans. Because of the advancement in tracking technology, people have become extremely careful regarding the content they share on emails.
However, AI helps look for unusual code language, patterns, or other communication methods that point toward an existing anomaly. It quickly identifies even a single message containing something uncommon to the remaining ones.
What Changes Will AI Bring in The Future?
AI will continue to influence ESI technology in the future in many impressive ways.
For instance, artificial intelligence will become more seamless with better integration. Instead of merely playing the role of an observer, it will suggest document codes or select documents that are high priority, simplifying the process even more.
Advanced technology will make it possible to use AI in the earlier stages of the electronic discovery reference model (ERDM). It will use data mining techniques even before somebody collects the data and offers suggestions on the most relevant prospects on a case.
The transformation of e-discovery by AI will continue in the future as it continually refines itself and gets smarter by reviewing more data daily. It will enable litigation teams to make discoveries and allow legal teams to make strategic decisions more efficiently.