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In the legal world, and in particular the world of electronic discovery, artificial intelligence (AI) has been around for more than a decade. It is no longer unusual or controversial for organizations to use AI technologies in litigation, especially where large or complex data sets are involved. Legal teams now routinely turn to AI to defensibly accelerate the process of identifying documents likely to be responsive to requests for evidence.

Innovations like technology assisted review (TAR), for example, rely heavily on machine learning and natural language processing to make connections and identify patterns within a body of data in a matter of seconds. This is work that would take even the most qualified human reviewers many, many hours to do manually, and with less accuracy.

Apart from sheer computing power, one of the most useful features of AI technology like machine learning is its ability to quickly learn and continuously improve the accuracy of its outputs with the essentially passive assistance of human reviewers. In continuous active learning (CAL), now a feature of leading eDiscovery platforms, even the process of training machines to find what youre looking for is performed algorithmically with no direction from human document reviewers beyond the coding or labeling they perform in the process of manual review. This is a remarkably efficient and cost-effective way to teach machines to identify responsive information, and it has enormous potential for other vital corporate functions. A notable example is compliance.

The usefulness of active learning as a proactive compliance and information governance tool has only recently begun to be explored and appreciated. Across the corporate landscape, reactive approaches to potential problems hidden in data stores are far more commonand ultimately more costly and risky. Companies will typically wait until a whistleblower complains or an employee happens upon a potential problem, and then respond by launching an internal investigation.

AI technology can help your organization avoid this scenario. You can use it to:

This handful of examples represents only a small fraction of potential use cases for AI in compliance and governance activities. Every industry will present a different set of use cases. Nevertheless, enterprises in just about every vertical face daunting compliance challenges requiring the identification of data-based risks in vast repositories of structured and unstructured data. This data is generated by hundreds or thousands of applications operating within diverse and often poorly integrated systems. This is the kind of environment where AI shines.

If your organization is already using an eDiscovery platform with built-in AI tools, it might make sense to explore how you can use those tools for broader data management, information governance, and risk mitigation purposes. As you run regular health checks, you will get a better understanding of your data and your approach to data-based compliance will be more proactive and cost-effective. That means fewer investigations in response to potential issues and, in many cases, less litigation overall.

About the Author

David Carns is the Chief Revenue Officer of Casepoint. He joined Casepoint as a Director of Client Services in 2010, rose the ranks to Chief Strategy Officer until his most recent promotion in 2019. In addition to being a recovering attorney, David possesses a lifelong passion for technology and its advancements. His career has always found him at the intersection of technology and the legal field given his intimate knowledge of both. Carns holds a Juris Doctorate from The John Marshall Law School and a Bachelors degree in Philosophy from DePauw University.

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Using Artificial Intelligence Tools to Run Proactive Health Check Investigations - insideBIGDATA