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Ensuring Defensibility in AI-Driven Document Review: Strengthening Coding Decisions

Buddy Fisher

Ensuring Defensibility in AI-Driven Document Review: Strengthening Coding Decisions

Introduction:

In the realm of eDiscovery, the integration of large language model AI like eDiscovery AI has revolutionized the document review process. As legal professionals increasingly rely on eDiscovery AI to replace manual document review, ensuring defensibility becomes paramount. In this blog post, we will delve into the factors that contribute to the defensibility of using eDiscovery AI for document review, with a specific focus on coding decisions. By implementing best practices and strategies, legal professionals can fortify the defensibility of their eDiscovery AI-driven document review, bolstering the overall credibility and reliability of the eDiscovery process.

Procedural Framework:

Establishing a robust procedural framework is crucial for ensuring the defensibility of AI-driven document review. Key considerations include:

a. Transparent and Documented Workflow: Maintaining a clear and well-documented workflow is essential. Legal professionals should document the steps involved in utilizing eDiscovery AI for document review, including data selection, instruction drafting, testing, validation, and quality control measures. Transparent workflows contribute to defensibility by allowing for scrutiny and auditing.

b. Sampling and Validation: Conducting random sampling of documents for validation purposes helps evaluate the accuracy and reliability of AI-driven coding decisions. Legal professionals should establish sampling protocols to ensure ongoing validation and quality control, providing evidence of a defensible approach.

Expert Involvement and Oversight:

Incorporating expertise and oversight into eDiscovery AI-driven document review strengthens defensibility. Key considerations include:

a. Subject Matter Experts (SMEs): Involving subject matter experts in the instruction drafting and validation process adds credibility and enhances defensibility. SMEs provide valuable insights, contribute to decision-making, and validate the accuracy and relevance of eDiscovery AI-driven coding decisions.

b. Continuous Monitoring and Improvement: Legal professionals should continually monitor the performance of eDiscovery AI models used for document review, ensuring they remain accurate and reliable. By staying updated on emerging legal standards and advancements in eDiscovery AI technologies, legal professionals can enhance the defensibility of their document review process.

Transparency and Explainability:

Transparency and explainability are vital for defending LLM AI-driven coding decisions. Key considerations include:

a. Explainable AI Models: Opting for eDiscovery AI models that offer interpretability and explainability helps ensure defensibility. Models that provide explanations for coding decisions enable legal professionals to understand the factors influencing those decisions, promoting transparency and facilitating the defense of those decisions, if needed.

b. Documentation of Rationale: Documenting the rationale behind coding decisions made by LLM AI models strengthens defensibility. Legal professionals should maintain records that outline the reasoning behind the selection of prompts, training data, and model settings, ensuring transparency and accountability.

Conclusion:

Ensuring defensibility in eDiscovery AI-driven document review is critical for maintaining the integrity and credibility of the eDiscovery process. By establishing a robust procedural framework, incorporating expert involvement and oversight, and prioritizing transparency and explainability, legal professionals can strengthen the defensibility of their AI-driven coding decisions. A defensible process not only withstands scrutiny but also enhances confidence in the eDiscovery outcomes, enabling legal professionals to leverage the benefits of eDiscovery AI while upholding ethical and legal standards.

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