Laws regulate business and have a special role in achieving societal objectives. However, they are ultimately a group of rules, a complex data set. Lawyering has always been driven by informational asymmetry, requiring deep knowledge and a superior ability in understanding how complex legislative and regulatory frameworks interconnect. Lawyers evolved to effectively analyse complex data and provide clients with reliable predictions, especially at times of great need or scarcity. This is a function of their access to sufficient data, cognitive limitations and ability to apply creativity to craft risk-balanced strategies.
As we digitised legislation and case law, it became clear that litigation lawyers’ document review work lent itself well to automation; algorithms could help produce cheaper and faster outcomes. It was time-consuming and expensive (therefore often prohibitive) for law firms to manually read and organise numerous documents into more useful or simply alternative formats as part of the discovery process.
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That process has been replaced by e-discovery tools, now a fact of a litigation partner’s life, with clients benefiting from lower costs and quicker turnaround times, without the need for armies of associates and paralegals.
What’s more interesting about machine learning technologies and analytics is their potential to produce superior and altogether new outcomes. The biggest insight to emerge from the application of AI to big datasets is that there is no longer a need to know why. By simply observing correlations, we surface valuable insights that are otherwise simply not possible.
These are actionable and reliable, such as predicting the direction and velocity of flu trends from internet searches to help the World Health Organisation plan vaccination deployment efforts. For technologists, cases and legislation are even more addressable through analytics as they are structured, with limited formats, aim to apply consistent logic rules, and generally do follow convention.
In part two, I’ll explore the different categories of use cases for predictive analytics in litigation, and the powerful social benefits they could bring.
