The Semantics of Machine Learning & Artificial Intelligence in eDiscovery

Published on October 28, 2019

Lia Majid

Lia founded Acorn Legal Solutions through the acquisition of the eDiscovery operations of Elijah Ltd in 2017. An industry outsider, Lia brought rigorous project management techniques to Acorn, similar to those used in the aerospace industry, which significantly improve client outcomes. Prior to Acorn, Lia was a Sr. Project Engineer at Parker Hannifin. She has a MBA from Kellogg School of Management at Northwestern University and Electrical Engineering and Mathematics degrees from Case Western Reserve University.

Precision Of Language Is Important – Or Is It?

I recently was asked to speak at the SoCal Women in eDiscovery Technology Symposium on the Future of AI + eDiscovery with Cat Casey from Disco (and previously Gibson Dunn) and Christine Milliron at Robins Geller. The panel talked about numerous predictive and automation technologies being adopted in legal services. But we spent the vast majority of our time discussing variations on Technology Assisted Review and Machine Learning in eDiscovery review applications.

Spirited debate on the differences between TAR, Machine Learning, and AI is a favored pastime of slightly tipsy conference veterans [1]. After the panel, an attendee posed a thought-provoking question to me, that I’ve continued to revisit over the past 3 weeks:

“Does drawing a distinction between Machine Learning and Artificial Intelligence really, ultimately, matter?”
I had very mixed feelings on the topic.

  • On the one hand, I work in an industry fundamentally based on the notion that precision of language is important. Linguistic shortcuts conflate concepts and prevent clarity of thought. Using the terms interchangeably today creates potential for miscommunications tomorrow.

  • Moreover, as a company laser-focused on value delivery, precision in our language is important towards our everyday commitment to setting clear, actionable expectations as to the services that we provide. At Acorn, we mean what we say — we have to; our clients are skilled (professionally trained!) at looking past pretense and fuzzy promises.

  • On the other hand, if sexy terminology drives market exposure to emerging technologies, then who cares about semantics? The pragmatist in me only cares about whether we, as an industry, are collectively raising the bar on efficiency and quality.
After three weeks of ruminating on the topic, I have somewhat ironically settled at a lawyer’s answer: It depends. And what it ultimately depends on is the audience. When I am talking to those who hold themselves out to be sophisticated actors in the space, I will use precision of language on topics like this as a measure of their actual expertise. Experts should have a point-of-view on this. However, when I am encouraging people to explore new technology solutions to old problems, I’ll stay more focused on their excitement than their diction.

Ultimately, eDiscovery is a very tactical field. So, in my day-to-day life, differences in AI semantics matter much less than being specific about technologies, workflows, budgets, timelines, and risks.


[1] I don’t even think there’s consensus on the definitions of either, other than that they’re vaguely related but still different. Try googling “difference between machine learning and AI” and see if you can make heads-or-tails of the results.


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About Acorn 

Acorn is a legal data consulting firm that specializes in AI and Advanced Analytics for litigation applications, while providing rigorous customer service to the eDiscovery industry. Acorn primarily works with large regional, midsize national and boutique litigation firms. Acorn provides a high-touch, customized litigation support services with a heavy emphasis on seamless communications. For more information, please visit