Sep 30, 2026 · 21 min · 8 segments
As AI becomes embedded across the enterprise, legal and compliance teams face new questions around discoverability, preservation, privilege, and the defensible use of AI in document review…
Therese CraparoGuest
Anthony DianaHostRobert CruzGuest
Michael RubayoGuestHey, so there's a lot to talk about regarding AI and both e-discovery as well as investigation.
And I think that as we approach some of these topics on the regulatory side, some of them are fairly novel, but it feels like we've got a little bit more underfoot within discovery as far as how firms are using advanced technologies, machine learning and the like, In the e-discovery process, court decisions to lean upon, plenty of writings and speeches from Judge Peck.
But Anthony, what are some of the key things that have already been established as far as the use of AI because of what we've been through with predictive analytics and TAR? Where do we stand right now? Yeah, look, I think,

you know, it was more than a decade ago, 15 years ago, when people were really, this was the big issue, right? Using TAR, predictive analytics, machine learning, and it was going to replace every docker reviewer, which obviously didn't happen.

There was a lot of discussion of, like, how do we make this work, right? There was a lot of dispute about it.

There was a lot of discussion about validation sets and all this kind of stuff.

But basically, I think the industry as a whole, with case law, developed really sound processes on how to use TAR, right? And validation was the most important thing, right? It's You have your seed set.

And then you always did validation and validation was critical to making sure that everyone knew that the tool was working, right? And cutoffs and all that kind of stuff.

Lots of best practices about continuous active learning and the like, and that was better.

So it was sort of developed over time, and I think everyone in the South pretty much comfortable with the use of tar, right? And we knew what the challenges were and whatnot.


I know there's recent case law that sort of said, well, it was part of the disclosure.

There was a lot of dispute about, well, we want to know more of what you did about using the relativity error in Gen AI and how you did it.
Hey, so there's a lot to talk about regarding AI and both e-discovery as well as investigation.
And I think that as we approach some of these topics on the regulatory side, some of them are fairly novel, but it feels like we've got a little bit more underfoot within discovery as far as how firms are using advanced technologies, machine learning and the like, In the e-discovery process, court decisions to lean upon, plenty of writings and speeches from Judge Peck.
But Anthony, what are some of the key things that have already been established as far as the use of AI because of what we've been through with predictive analytics and TAR? Where do we stand right now? Yeah, look, I think,

you know, it was more than a decade ago, 15 years ago, when people were really, this was the big issue, right? Using TAR, predictive analytics, machine learning, and it was going to replace every docker reviewer, which obviously didn't happen.

There was a lot of discussion of, like, how do we make this work, right? There was a lot of dispute about it.

There was a lot of discussion about validation sets and all this kind of stuff.

But basically, I think the industry as a whole, with case law, developed really sound processes on how to use TAR, right? And validation was the most important thing, right? It's You have your seed set.

And then you always did validation and validation was critical to making sure that everyone knew that the tool was working, right? And cutoffs and all that kind of stuff.

Lots of best practices about continuous active learning and the like, and that was better.

So it was sort of developed over time, and I think everyone in the South pretty much comfortable with the use of tar, right? And we knew what the challenges were and whatnot.


I know there's recent case law that sort of said, well, it was part of the disclosure.

There was a lot of dispute about, well, we want to know more of what you did about using the relativity error in Gen AI and how you did it.
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