Since we released our text summarization resources, the legal technology community has shown interest in leveraging summarization technology to support litigation document review, deposition digests, and contract analysis. Data scientist interest to use machine learning to mine legal document corpuses and support legal strategy was also one factor motivating our summarization research. The time is therefore ripe for data scientists to apply new text analytics capabilities for legal use cases. But to be effective, data scientists must first understand how lawyers think: what problems they're trying to solve, how their processes are structured, and, perhaps most importantly, what fears may hinder the adoption of new technologies. This guest blog post from Dean Gonsowski, kCura's VP of Business Development, provides tips to help data scientists explain the value of machine learning to lawyers.
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.
The intersection of law and IT is a busy place. Litigation and investigations are surging, fueled by regulatory compliance mandates (including data privacy laws), even as sophisticated cyberattacks target that very information--resulting in more investigations and litigation. Legal departments and law firms are beset by tight budgets and constrained IT resources. The pandemic added further complications, forcing lawyers to work remotely, while spurring increased legal activity. Caught in the middle is the vital task of electronic discovery, which is being stretched to the limit by unprecedented volumes of data.
The legal services industry is hurtling headlong into a revolution in the way that we carry out virtually every aspect of our jobs. The introduction of artificial intelligence (AI) – intelligence exhibited by machines that are trained to learn and solve problems – is not just an extension of prior technologies. AI holds the potential to dramatically change the field in a variety of ways, from reducing bias in investigations to challenging what evidence is considered admissible. For corporate legal department teams that are prepared to embrace the power of AI, there is vast potential for increased corporate security, greater productivity in litigation management and improved corporate investigations capabilities. Corporate legal departments, no matter how large or small, can no longer escape the fact that AI capabilities are real.
Technological advancements are significantly influencing the legal services landscape. At unprecedented rates, corporations, law firms, and state and federal enforcement agencies are accepting and adopting the use of advanced technology in legal matters, including automation, machine learning, and algorithm-driven data analytics. With respect to discovery, over the past decade, the expansion of technology-assisted review has been well documented and debated. The wide embrace of technology-assisted review – or "TAR" for short, has met with acclaim from clients and their counsel. It is essentially undisputed by now, for instance, that TAR has proven to help produce quality results, while also achieving quantifiable cost savings.