Course Content
Module 1: Current Challenges in eDiscovery
Traditional eDiscovery Pain Points The Data Deluge: Volume, Velocity, Variety Cost and Time Implications Human Error and Inconsistency
0/6
Module 2: How AI Supports Document Review
Introduction to AI in Legal Machine Learning Fundamentals for eDiscovery Natural Language Processing (NLP) in Document Review AI-Powered Document Classification and Tagging
0/4
Module 3: Predictive Coding & Clustering
Understanding Predictive Coding (Technology Assisted Review - TAR) Active Learning Workflows Clustering for Conceptual Grouping Sampling and Validation Techniques
0/4
Module 4: Producing with Precision: Bates Stamping & Redaction
Automated Bates Stamping with AI AI-Assisted Redaction for PII/PHI Quality Control and Verification of AI Outputs
0/3
Module 5: Integrating with LegalGPTs eDiscovery Tools
Overview of LegalGPTs Platform Hands-on with LegalGPTs AI Features Workflow Integration Strategies Case Studies: Success Stories with LegalGPTs
0/4
Module 6: Cost Savings and Compliance Considerations
Quantifying ROI of AI in eDiscovery Ethical Considerations of AI in Legal Practice Data Security and Privacy with AI Tools Regulatory Compliance and Best Practices
0/4
AI for eDiscovery: From Collection to Production

The digital age has ushered in an unprecedented  amount of data. Organizations generate and store vast quantities of ESI, including emails, instant messages, social media posts, cloud-based documents, and more. This “data deluge” is characterized by:

Volume: The sheer quantity of data that needs to be processed and reviewed.
Velocity: The speed at which new data is generated and accumulated.
Variety: The diverse formats and sources of ESI, ranging from structured databases to unstructured text and multimedia files.

This exponential growth in data makes traditional eDiscovery methods increasingly unsustainable.