In the last decade, there has been a lot of hype surrounding Artificial Intelligence (AI) in the ediscovery industry, but is it reality or all just science fiction?
AI, in layman’s terms, is the ability of a computer (or robot controlled by a computer) to perform tasks requiring human intelligence and discernment. While many confuse the difference between the two, machine learning (ML) is a type of AI that gives computers the ability to learn without being explicitly programmed. Instead, the computer uses human input to make educated guesses that extend beyond that input. Machine learning is the foundation of the predictive coding technology that has transformed ediscovery, reducing the amount of reviewable data by as much as 80% in some matters.
Many solutions claim that using machine learning models can significantly aid in making the ediscovery process more efficient, but is it a reliable method? Messaging promises so many amazing results when using AI capabilities in the discovery process, such as:
Automated document review of thousands of documents in a matter of seconds.
Instant intelligent recommendations for legal teams.
The ability to discover patterns in documents invisible to the human eye.
New and exciting discoveries made with constantly learning ML models.
While these remarkable results are possible, some unrealistic expectations continue to flourish throughout the ediscovery industry.
Unrealistic Expectations of AI in Ediscovery
There are plenty of solutions that over promise what AI can actually do to improve ediscovery, including its ability to get exact predictions in a fraction of the time. Even though AI can dramatically streamline the ediscovery process, it is not entirely automated, and legal professionals need time to train models properly. Other issues may occur as well, including:
Data used to make predictions may not be accurately reviewed or fully representative of a dataset, particularly when the data is pulled from a biased search.
Predictions can be prone to errors, making thought-out and agreed-upon metrics an essential component of defensibility in court.
In reality, both unsupervised and supervised ML models have limitations related to human effort. In unsupervised models, the model isn’t learning from human judgments, so the intended use case needs to be considered carefully. On the other hand, human input and bias pose limitations to supervised ML models since the accuracy of the model partially depends on the accuracy of the training data.
The Real Value AI Can Deliver Today
Despite the misconceptions about the impact of AI technology on the world of ediscovery, its benefits cannot be understated. Ediscovery vendors have utilized this type of technology to enable legal professionals to handle various use cases, including creating repeatable processes, managing costs, expediting workflows, and ultimately, improving the quality of legal work.
Here are a few of the modern and specific applications of AI technology during the ediscovery process:
Enhancing the document review process. With machine-learning tools, AI-driven platforms can make it easier to sift through large datasets by separating relevant and irrelevant documents, prioritizing documents for further review, and uncovering specific trends in the data.
Protecting sensitive data. The protection of private data is a top priority of organizations as cybersecurity threats, increased government intervention (CCPA, GDPR, etc.), and the introduction of new regulations (e.g., the SEC’s new proposed rules) have become the norm within the legal industry. Platforms with AI technology can help with identifying potential cybersecurity risks and eliminate issues associated with data breaches.
Executing non-discovery tasks. AI technology can also help legal professionals across various industries complete different forms of legal work, including but not limited to internal investigations for corporations, early case assessment for law firms, and managing Freedom of Information Act (FOIA) requests for government agencies. Legal teams can leverage ediscovery platforms to eliminate manual, tedious tasks by utilizing machine learning software to create repeatable workflows.
AI’s Near Term Future in Ediscovery
AI-powered feature adoption will continue to rise in the ediscovery landscape because AI empowers attorneys with a more automated and efficient document review process when handling massive amounts of data. Given how quickly AI has integrated itself into the ediscovery process, the common notion is that AI will continue to appear in more and more parts of the ediscovery process, including:
Automated audio/video and metadata redaction.
Automated recommendations in case deposition tools.
Recommended time periods for optimal document review and workload prioritization.
Improvements to concept clusters during document review.
Communication pattern analysis.
Transferring predictive coding models from one project to another.
Automation technology will increasingly influence decision-making from the top down. Whether it’s prioritizing documents to review by leveraging predictive coding or using AI to make the ediscovery process more efficient, legal teams will increasingly utilize automation to speed up otherwise time-consuming workflows.
Want to learn more about how the Everlaw platform will help your team adapt to the rapidly changing face of the legal industry? Download your free copy of “Lighting the Way: 2021 Ediscovery Trends & Everlaw’s 2022 Predictions” today.