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cover of episode 901: Automating Legal Work with Data-Centric ML (feat. Lilith Bat-Leah)

901: Automating Legal Work with Data-Centric ML (feat. Lilith Bat-Leah)

2025/7/1
logo of podcast Super Data Science: ML & AI Podcast with Jon Krohn

Super Data Science: ML & AI Podcast with Jon Krohn

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Jon Krohn: Epic是一家大型法律科技公司,拥有超过6000名员工。Epic AI Discovery Assistant声称可以自动化超过80%的传统电子取证流程,并完成审查比传统技术辅助审查快90%。我想了解线性审查、TAR和电子取证等术语,以及AI如何简化流程。 Lilith Bat-Leah: 我提供的软件确实支持TAR工作流程。TAR是一种使用机器学习将文档分类为与诉讼相关或不相关的过程。诉讼涉及大量的文档、电子邮件和各种非结构化数据。律师需要审查这些数据,以确定哪些需要提交给对方。电子取证是指以电子方式存储的业务记录的发现过程。机器学习工具使对方律师更容易找到关键证据。Epic AI Discovery Assistant利用LLM来更快地找到相关文档。你的自然语言指令和标记示例将用于训练最佳分类器。每个案例都需要一个单独的分类器,具体取决于需要分类的不同事项的数量。通常,你将始终具有响应能力模型,基本上是相关性模型。你可能还会对诸如特权、保密性以及律师关心的所有问题进行分类。法律行业中,评估指标的严格性非常重要,因为评估指标有时会与对方律师或政府机构协商。作为一名数据科学家,你必须能够解释评估指标的真正含义以及可能对律师和法官产生的影响。

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Senior Director of AI Labs for Epiq Lilith Bat-Leah speaks to Jon Krohn about the ways AI have disrupted the legal industry using LLMs and retrieval-augmented generation (RAG), as well as how the data-centric machine learning research movement (DMLR) is systematically improving data quality, and why that is so important. 

Additional materials: ⁠⁠⁠⁠⁠www.superdatascience.com/901⁠)⁠⁠⁠

This episode is brought to you by the ⁠⁠__Dell AI Factory with NVIDIA__⁠⁠) and __Adverity, the conversational analytics platform__⁠⁠⁠).

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

In this episode you will learn:

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(05:45) Deciphering legal tech terms (TAR, e-discovery)

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(13:47) How legal firms use data and AI

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(29:01) All about data-centric machine learning research (DMLR)

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(46:58) Lilith’s career journey in the AI industry