AI Driven Predictive Coding Workflow for eDiscovery Success

Predictive Coding for eDiscovery

1. Initial Case Assessment

1.1 Define Objectives

Identify the goals of the eDiscovery process, including the scope of data to be reviewed and the desired outcomes.

1.2 Data Collection

Gather relevant data sources, including emails, documents, and databases. Ensure compliance with legal and regulatory requirements during data collection.

2. Data Processing

2.1 Data Ingestion

Utilize AI-driven tools such as Relativity or Everlaw to ingest collected data into a centralized platform for processing.

2.2 Data Cleaning

Employ AI algorithms to filter out irrelevant or duplicate data, ensuring only pertinent information is retained for review.

3. Training the Predictive Coding Model

3.1 Sample Data Selection

Select a representative sample of documents for initial coding. This sample will be used to train the predictive coding model.

3.2 Manual Review and Coding

Legal professionals manually review the sample documents, categorizing them as relevant or irrelevant. Tools such as Logikcull can assist in this process.

3.3 Model Training

Utilize AI tools like Brainspace or Everlaw to train the predictive coding model based on the manually coded sample. The model learns to identify patterns associated with relevant documents.

4. Predictive Coding Implementation

4.1 Full Data Review

Deploy the trained predictive coding model on the entire dataset. The AI will categorize documents based on the learned patterns.

4.2 Continuous Learning

As additional documents are reviewed, continuously refine the predictive coding model by incorporating feedback and new data. Tools like OpenText can facilitate this iterative process.

5. Quality Control

5.1 Validation of Results

Conduct a quality control review to ensure the accuracy of the predictive coding results. This may involve a secondary manual review of a subset of documents.

5.2 Adjustments and Refinements

Make necessary adjustments to the model based on validation outcomes to enhance accuracy and reliability.

6. Reporting and Documentation

6.1 Generate Reports

Utilize reporting tools within the eDiscovery platform to generate comprehensive reports detailing the findings and the effectiveness of the predictive coding process.

6.2 Documentation of Processes

Document the workflow and any decisions made during the predictive coding process for future reference and compliance purposes.

7. Final Review and Production

7.1 Final Review

Conduct a final review of the relevant documents identified by the predictive coding model to ensure completeness and compliance.

7.2 Document Production

Prepare and produce the final set of documents for legal proceedings, ensuring all materials meet the necessary formatting and submission requirements.