The purpose of this guide is to teach machine learning concepts — not to an audience of future ML Engineers, Analysts, and Data Scientists — but to lawyers increasingly required to have a thorough understanding of emerging technologies like machine learning to do their jobs in a meaningful way.
To build strong and compelling cases, litigators require an understanding of the human decisions made during the development of a machine learning model. It is not necessary to learn to code or understand advanced mathematics to build an effective litigation case. For this reason, all of our machine learning content includes no code and minimal mathematics.
Before we overstate our case and likely antagonize a large group of people we respect, we must clarify that math is important. And there are situations where an advanced understanding of mathematics is essential. People at companies like Google and Facebook are pushing the boundaries of machine learning and working on bleeding-edge tools. These people almost certainly employ calculus, linear algebra, and more advanced math routinely in their work.
We don't intend to be an in-depth explanation of every possible aspect of training an ML model. However, more of a gentle introduction to basic concepts and a high-level overview of the process itself, particularly beyond the data collection stage until model deployment. Our goal is to fill the knowledge gap that lawyers have when preparing their litigation cases. Our content stems from extensive user interviews and research of lawyers around the globe.
We focus mainly on the steps of the machine learning process after data collection. This guide focuses primarily on exploratory data analysis — where a dataset is used to develop a machine-learning algorithm running in the real world, making predictions that influence decisions.
We are committed to developing the platform in whatever way is most useful for AI litigation cases worldwide. As the consensus of knowledge within the litigation community grows towards a deeper understanding of machine learning, we may cover more advanced techniques and provide more nuance in our explanations.
And with that, please head over to the next article in this guide, Introduction to Machine Learning, where you'll learn about the landscape of machine learning and the specific steps in the process of training a machine learning algorithm.
If you notice anything incorrect or missing from our explanations, please let us know through the contact form! We want our content to be as accurate and useful as possible — and you can help us do that. If you've worked on a case that would be a good fit for our platform, please submit it through our contact form.