AI Driven Predictive Coding Workflow for E Discovery Solutions

Predictive Coding for E-Discovery

1. Initial Assessment

1.1 Define Scope

Identify the specific legal case and the types of documents required for review.

1.2 Data Collection

Gather all relevant electronic data from various sources such as emails, documents, and databases.

2. Data Processing

2.1 Data Ingestion

Utilize AI-driven tools like Relativity or Logikcull to import and organize the collected data.

2.2 Data Cleaning

Implement data cleaning processes to remove duplicates, irrelevant files, and non-responsive documents.

3. Predictive Coding Implementation

3.1 Training the AI Model

Use a subset of manually reviewed documents to train the predictive coding model. Tools such as Everlaw or Brainspace can facilitate this process.

3.2 Continuous Learning

As new documents are reviewed, the AI model should continuously learn and adapt to improve accuracy.

4. Document Review

4.1 Automated Review

Leverage AI capabilities to automatically categorize and prioritize documents based on relevance and responsiveness.

4.2 Human Oversight

Incorporate a team of legal professionals to validate AI classifications and ensure compliance with legal standards.

5. Quality Assurance

5.1 Performance Metrics

Monitor the effectiveness of the predictive coding model using metrics such as recall, precision, and F1 score.

5.2 Iterative Refinement

Refine the model based on feedback and performance metrics to enhance accuracy and efficiency.

6. Final Review and Reporting

6.1 Final Document Set

Compile the final set of documents for legal review and submission.

6.2 Reporting

Generate comprehensive reports detailing the process, findings, and outcomes using tools like CaseGuard or Zapproved.

7. Post-Implementation Review

7.1 Evaluate Process

Conduct a review of the entire predictive coding process to identify areas for improvement and lessons learned.

7.2 Stakeholder Feedback

Gather feedback from all stakeholders involved to enhance future e-discovery projects.