The past several years have seen the explosion of machine learning in law, particularly in e-discovery where technology-assisted review (TAR) gave way to an upgraded TAR 2.0, which itself gave way to continuous active learning (CAL) and TAR 3.0. This use of AI looked to introduce speed and cost efficiency to the discovery process by whittling down documents from the beginning, while still maintaining eyes-on final review.
But the next step for some in e-discovery is asking: Why does every litigation need to be different? One way some are looking to save even more time is through the use of AI model libraries—essentially, reusing algorithmic models in multiple matters, with the ability to store and select between different models in future litigations. Those who have reused models across matters say the algorithms need to be narrowly defined to a specific matter type, but if the model fits, it can help jumpstart the review process with less training time.