If you're interested in GenAI Tar -which may not yet make financial sense, you should also give traditional TAR 1 another look in the meantime. Check out the paper for discussion on why, and how these workflows compare.
Why is there so much excitement over GenAI’s potential to replace human document review, while traditional TAR that replaces human review (“TAR 1”) has been less favored?
GenAI, when used to predict review tags, shares many of the same benefits and drawbacks as traditional TAR 1.
Learn what practitioners should keep in mind when using GenAI-based TAR, and how traditional TAR 1 may compare here: https://lnkd.in/e2xTrA_Q#LegalTech#GenAi#TAR
I really appreciate all the papers you are contributing to eDiscovery space. These should be mandatory reading for everyone in eDiscovery. I can almost gauge practitioner competency in the eDiscovery space based on their knowledge of papers by Lewis or Pickens
Why is there so much excitement over GenAI’s potential to replace human document review, while traditional TAR that replaces human review (“TAR 1”) has been less favored?
GenAI, when used to predict review tags, shares many of the same benefits and drawbacks as traditional TAR 1.
Learn what practitioners should keep in mind when using GenAI-based TAR, and how traditional TAR 1 may compare here: https://lnkd.in/e2xTrA_Q#LegalTech#GenAi#TAR
Pondering the debate over TAR vs GenAI? Our latest paper reveals why this question may be wrong. Discovery why GenAI, when applied to TAR, is TAR --specifically, TAR 1. With insights that bridge “traditional” and GenAI TAR, our paper shows why GenAI TAR does not affect defensibility, and is not a new process- just a new engine in the vehicle that is TAR.
Read the full paper on Redgrave Data's TAR 1 Reference Model here: https://lnkd.in/e2xTrA_Q#LegalTech#GenAI#TAR
Introducing the TAR 1 Reference Model: a new tool for the eDiscovery industry in navigating the complexities of TAR document reviews. It applies to any review that relies on machines to tag documents --whether traditional or GenAI algorithms. For those preparing to use GenAI for TAR review, the reference model will help practitioners ensure their process follows established, defensible review steps.
Download the paper here: https://lnkd.in/e2xTrA_Q#DataDoneDifferently#legaltechnology
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#AI#ML#Tech How to Think About Using Your Company’s Information with GenAI: Assessing the options for GenAI information integration along with considerations for your specific company
Continue reading on Towards Data Science » #MachineLearning#ArtificialIntelligence#DataScience
Remember that paper from Redgrave Data that gave us some real-life benchmarking of #GenAI performance in a #TAR review?
(If not, let me refresh your memory: https://lnkd.in/ez5TxHYz)
Remember how I noted that the shape of the results looked very TAR 1.0-esque?
Well, now Tara Emory and Jeremy Pickens from that same team at Redgrave, along with their colleague Wilzette Louis, have taken it a step further and generated a full GenAI TAR 1 Reference Model, laying out in a clean visual both the traditional TAR 1.0 approach and a modified version delineating where LLM prompting can be inserted into the workflow.
While I rarely see TAR 1.0 workflows implemented in my own practice (TAR 2.0 is typically better suited to our needs), it more easily lends itself to statistical measurement and validation than active learning and is still very useful when working with especially large data sets. This feels like an important step toward integrating GenAI into #ediscovery in a systematic way and generating best practices for use.
The full paper will appear in the upcoming The Sedona Conference Journal https://lnkd.in/ejBRmRjPhttps://lnkd.in/evffU5x8
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Director of eDiscovery at Brownstein Hyatt Farber Schreck
5moI really appreciate all the papers you are contributing to eDiscovery space. These should be mandatory reading for everyone in eDiscovery. I can almost gauge practitioner competency in the eDiscovery space based on their knowledge of papers by Lewis or Pickens