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

While AI tools offer significant advantages, robust quality control and verification processes are essential to ensure the accuracy and defensibility of their outputs. This includes:

Human-in-the-Loop Review: Human reviewers still play a critical role in validating AI-generated classifications, redactions, and Bates stamps.
Audit Trails: Maintaining detailed logs of AI actions and human overrides for transparency and accountability.
Sampling and Spot-Checking: Regularly sampling AI-processed documents to identify and correct any errors or inconsistencies.