Contexts for Doing Legal AI

When it comes to doing Legal AI, it’s important to understand the context of where the AI work is being carried out. There are two main contexts for doing AI:

1. AI in a research context

1. AI in an industry context

AI in a Research Context

AI in research is usually carried out in university departments and at big tech R&D labs. It involves designing new architectures, pushing forward the frontiers of knowledge and stepping into the unknown. Much of the work is done through experiments under ideal circumstances with large datasets and virtually unlimited budgets and resources.

AI in an Industry Context

AI in industry is quite different. Datasets are rarely ever clean and complete, there are stakeholders to manage and everything has to be done yesterday. Much of the AI work in industry is applied AI. The goal is not to venture into the unknown and develop something completely new. Rather, the focus is on taking what has already been created and applying it to new and different problems in an industry context.

Context when Hiring

This distinction is especially important when hiring. Someone with a PhD and years of experience in a research lab might be great at doing AI in a research context but would struggle in an industry context. A completely different skill set is needed for doing AI in an industry context.

From Research to Industry

It’s important for folks doing AI in an industry context to be aware of and keep up to date with the developments around AI in a research context. At the moment, AI in an industry context is informed by AI in a research context. Ultimately, AI in a research context dictates much of what happens around AI in an industry context.

From Industry to Research

There is an argument to be made that a more beneficial, pragmatic and ultimately useful approach would be for AI in an industry context to dictate and guide AI in a research context.

Legal AI - Industry Context

Across a particular industry, there are common use cases and problem verticals. In the legal sector, for example, Knowledge Management is one of these such verticals.

A more fruitful approach would be to carry out AI in a research context informed by an industry use case like Knowledge Management so that the solutions developed would be immediately practical and useful.

Part of our vision is to work towards a future where AI in a research context is informed and dictated by use cases in the legal industry.