Sometimes, technology solves problems, sometimes it creates them. Thanks to technology, privilege logging is unwieldy. Can AI help?

That’s the question being asked by Rhys Dipshan of Legaltech^® News (Technology Made Privilege Logging Unwieldy. Can AI Fix It?, available here).

How big is the issue? “When I got on the bench in 1995, the privilege logs in a typical case [were] two to three pages, maybe 50-100 entries,” former U.S. Magistrate Judge Andrew J. Peck said during the Legalweek 2024 Judges Debate keynote in January. “Now the privilege logs are like little novels, and there may be 10,000 or more entries. That is very expensive and is often useless to the other side in figuring out what is or isn’t privileged.”

It’s no coincidence that privilege log volume grew alongside a technological transformation in the industry that saw legal communications move from paper memos and faxes to emails and messaging apps. Now, legal teams are struggling to rein in all the new ESI—and privileged content—they create.

“Log volume, in my view, is entirely related to the massive growth and the overall volume of communications, both by individuals and businesses,” said Robert D. Keeling, partner at Sidley Austin, and founder and head of the firm’s e-discovery and data analytics group.

He explained, “For example, on the amount of privileged documents in a document population, we have not seen the percentages change over time. Instead, the volume, however, of communication has grown as employees and businesses transitioned first to email and later to applications and messaging applications, such as Slack or Microsoft Teams.”

Well, if technology got us into this mess, can technology – through AI – help get us out of it by helping to automate the privilege designation process?

At least some the experts say no – at least not yet. Especially with the more nuanced types of privilege beyond just attorney-client communication.

Keeling stated that “identifying privileged documents in a population is more difficult … than a traditional responsiveness review. And the reason is because privilege is typically a more nuanced problem and a more difficult issue for traditional AI and data analytics to confront.”

Scott Milner, partner at Morgan, Lewis & Bockius and co-leader of the firm’s eData practice stated: “I’ve seen [no] models that are focused on…industry specific privileges” like “the bank examiner privilege and confidential supervisory information (CSI), as well as suspicious activity reports (SAR)” that you see in the financial services industry.

And Judge Peck pointed out another concern – cost, telling Legaltech News that the expense of ingesting large datasets into advanced AI tools for privilege review can be prohibitive for many legal teams. “It’s sort of like where we were with TAR 12 years ago, in that it’s more expensive to run,” he said.

Even though there’s more promise with using the technology—specifically generative AI—to draft the privilege logs themselves, there’s still room for improvement there as well. But the experts also note the technology is fast improving.

Some terrific points raised in the article to keep in mind when talking with vendors who tout AI privilege review capabilities. Can it handle different forms of communication, like Slack and Teams? Can it handle privilege determination beyond just attorney-client communication? What does it cost? These are important questions to ask.

Privilege logging may be unwieldy, but AI isn’t fixing it all just yet. But stay tuned – things are changing rapidly. A year from now (or sooner), things may be different.

So, what do you think? Do you agree that we’re not there yet with AI helping to automate the privilege designation process? Please share any comments you might have or if you’d like to know more about a particular topic.

Image created using GPT-4’s Image Creator Powered by DALL-E, using the term “robot lawyer looking at a privilege log”. Sure, DALL-E can’t spell “privilege”, but the expression on the robot lawyer’s face is priceless! 😀

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