There is no denying that the technological revolution has had a substantial impact on every major industry, including the legal sector. While many technologies have influenced the legal landscape, one is altering the litigation discovery process: Continuous Active Learning, or CAL. It has had the most significant impact on the document review process, so much so that it has become a necessity in the current legal ecosystem.
What Is CAL?
Artificial Intelligence (AI) is increasingly being used to analyze documents and predict whether they’re relevant to specified criteria. CAL is the most effective and popular method of training AI for use in eDiscovery. As the name suggests, the model learns continuously: It updates its predictions regularly with new coding decisions from human reviewers. While there are no other technologies quite like CAL, it’s has similarities to the popular music application “Pandora.”
When using Pandora, the application monitors the artists and genres of music that you listen to most frequently while providing a “thumbs up” or “thumbs down” to specific songs. Over time, Pandora presents you with more of the songs and artists that you like while screening out music that you have responded negatively to in the past. Similarly, CAL learns what is relevant from reviewer feedback, makes suggestions based on that feedback, and updates its predictions until it reliably provides reviewers with relevant documents. It is also active in its own learning process, selecting documents for human review that will be most helpful in improving its predictions.
This tool can be used in a variety of innovative and valuable ways, but it is still not widely adopted. A recent survey found that 36 percent of practitioners were not using it. CAL can be a powerful tool to help practitioners and their clients. The technology helps attorneys honor their fiduciary duty to their clients by verifying the thoroughness of the discovery process and providing faster access to insights and evidence.
Use Cases for CAL
CAL solutions are not meant to replace the human element in document review or litigation discovery. They enhance the effectiveness of the human component by building a predictive model to rank documents based on relevance. These rankings can then be used to augment or supplement the review process.
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