Active learning is a key component of predictive coding. In an active learning workflow, the AI system identifies documents that it is most uncertain about and presents them to human reviewers for coding. This feedback loop allows the AI to continuously refine its understanding and improve its accuracy. The process continues until the AI model reaches a desired level of stability and accuracy, or until a sufficient number of relevant documents have been identified.
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