Extract from an article by eDiscovery expert Mike Quartararo from Above The Law
Are Humans The Weak Link In Technology-Assisted Review?
A few days ago, I began wondering what is known to be true about TAR that everyone in the eDiscovery space should be able to agree upon.
First, TAR is not artificial intelligence. I know, I know, some folks have taken to generally lumping TAR under the general umbrella of AI-related tools. And I get it. But when you cut through the chaff of the marketing hype, TAR is machine learning — nothing more, nothing less. It’s the same machine learning that’s been used since the 1960s to analyze documents in other industries. There’s nothing artificially intelligent about TAR. It does not think or reason on its own. TAR applications analyze the content of files and categorize files based on the examples used to “train” the software. In other words, you get out of a TAR project exactly what you put into it. Anyone who says otherwise is either not being honest or just doesn’t know any better.
Second, TAR works. Whatever tool you’re using, whichever algorithm is deployed, whether it’s active or passive learning, supervised or unsupervised, the bottom line is the technology works. TAR applications effectively analyze, categorize, and rank text-based documents.
Third, using a TAR application — any TAR application — saves time and money and results in a reasonable and proportional outcome.
If there is any shortcoming of TAR technologies, the blame may fairly be placed at the feet (and in the minds) of humans.
- Relatively Speaking: Predictive Coding Technologies and Protocols Survey Results
- TAR vs. Keyword Search Challenge, Round 3 (Bill Dimm)