Legal AI Development Checklist1 min read

Building a Legal AI application is a complex multi-faceted process comprising different stages.

Our internal development experience at Synch, the tech law firm behind Legal AI Blog, has proved that it is possible to build a useful AI product in-house as long as the following criteria are met:

A the application’s focus is clearly defined
B necessary data are collected and cleaned by relevant experts, i.e. those who understand the data
C the collected and cleaned data are well-organized and labelled
D the datasets are checked for consistency before training using an efficient sanity-check algorithm, e.g. for avoiding data duplicates etc.
E multiple AI models have been tested, and the best-performing AI model is selected for the task
F if a set of tasks is performed on the same data, multiple AI models have been tested for each task (the same model may perform differently depending on the task)
G the selected best-performing model has been tweaked using an efficient fine-tuning algorithm, with the goal to reach the highest accuracy within a reasonable timeframe
H internal testing by different user groups has resulted in a well-documented and non-biased feedback
I training and deployment routines are well-documented
J tools for continuous training (i.e. improvement) of the AI model have been developed, and the routines have been thought through

We at Synch are working on a number of legal AI solutions. Stay tuned for updates!

Sergii Shcherbak