Chess, Not Checkers: The Human Element

In our last post, we discussed how application of generative AI can help to keep costs manageable and increase flexibility in your strategic approaches to complex litigation.

However, the use of generative AI in complex litigation can lead to significant challenges if it operates without adequate human supervision. GenAI solutions could inadvertently produce incorrect or misleading outputs due to biases in training data, insufficient context understanding, or inherent limitations in the algorithms. For example, GenAI could misinterpret legal terms or fail to recognize nuanced privilege considerations during document review, leading to inadvertent disclosure of sensitive or privileged information. Without human oversight to validate outputs, such mistakes could compromise the integrity of the litigation process, result in legal sanctions, and/or cause reputational harm to the parties involved.

GenAI based processes might also unintentionally expose sensitive data if not properly configured or if it generates outputs that include confidential details. To safeguard the defensibility and security of litigation workflows, effective human supervision is necessary to ensure that AI outputs are accurate, contextually appropriate, and compliant with legal and ethical standards. In this post, we will discuss how to balance innovation with ethics and best practices to ensure human oversight in AI-driven processes for a defensible approach to litigation that also maximizes protection of sensitive data.

The Human Element in AI-Driven Processes

Balancing innovation with ethics and best practices in AI-driven processes for litigation involves carefully integrating technology with human oversight to create defensible workflows while protecting sensitive data. Here are six components of a structured approach with an identified best practice for each:

Embrace a “Human-in-the-Loop” Model

A “Human-in-the-Loop” (HITL) approach ensures that critical decisions in litigation are consistently subject to human review, reducing the risks associated with over-reliance on automated processes. This approach integrates human oversight into AI-driven workflows, combining the efficiency of technology with the judgment and expertise of legal professionals to maintain defensibility and accuracy.

In the “HITL” approach, humans play a key role in validating AI predictions or categorizations, particularly in sensitive areas like privilege or relevance determinations. They can develop specific additional methodologies to validate the work of the AI machine, such as elusion testing and random sampling. They also address exceptions or ambiguities where AI lacks confidence, ensuring nuanced cases are handled appropriately. Additionally, human oversight provides accountability by maintaining a clear audit trail of all decisions made or reviewed, ensuring transparency and trustworthiness in litigation workflows.

Best Practice: Design workflows where AI suggestions are reviewed by trained professionals employing complementary technology, ensuring quality control, and minimizing potential mistakes.

Align Innovation with Ethical Standards

AI innovation should align with ethical principles such as fairness, accountability, and transparency. This involves actively mitigating bias by testing and monitoring AI models to ensure equitable outcomes, using interpretable AI models or incorporating mechanisms that enable practitioners to understand decision-making processes, and maintaining thorough documentation of how AI tools are trained, validated, and deployed to defend your approach when leveraging AI. Two examples of ethical guidelines used to navigate innovation are:

• Findable, Accessible, Interoperable, Reusable (FAIR): The FAIR principles provide a framework for managing and sharing data to ensure it is machine-actionable and beneficial across disciplines. These principles emphasize the need for data to be easily located through unique identifiers and rich metadata (Findable), retrievable through standardized protocols with clear access conditions (Accessible), compatible with other systems using common formats and vocabularies (Interoperable), and well-described to enable reuse in various contexts with clear licensing and provenance (Reusable). Together, they promote transparency, collaboration, and the efficient use of data.
• Fairness, Accountability, Transparency (FAT): The FAT principles are ethical guidelines for the development and deployment of AI systems to ensure they operate responsibly and equitably. These principles advocate for mitigating bias and preventing discrimination in AI outcomes (Fairness), holding developers and organizations responsible for the impacts of their systems with mechanisms for redress if harm occurs (Accountability), and ensuring systems are understandable and explainable to users and stakeholders by sharing information about algorithms, data, and decision-making processes (Transparency). Together, they aim to promote trust and integrity in AI technologies.

Best Practice: Regularly evaluate AI tools against ethical guidelines and legal standards, particularly in sensitive contexts like privilege review or data privacy.

Enhance Data Protection Mechanisms

AI-driven processes, particularly in eDiscovery where large volumes of data are analyzed, can pose risks to sensitive information, requiring careful management to mitigate potential vulnerabilities. Key strategies include leveraging AI for data minimization by filtering and processing only the data essential for litigation, utilizing secure platforms with robust encryption, access controls and monitoring tools to protect sensitive information, and ensuring compliance with data privacy regulations such as GDPR to maintain legal and ethical standards.

Best Practice: Regularly audit AI systems to ensure compliance with data protection standards and maintain a defensible position in litigation.

Prioritize Training and Collaboration

Effective human oversight depends on the capabilities and collaboration of those involved, requiring comprehensive training to equip legal teams with the skills to interact effectively with AI systems. It also involves fostering cross-functional collaboration among IT, data governance, and legal professionals to design and monitor AI workflows seamlessly. Additionally, incorporating iterative feedback loops ensures continuous improvement of AI performance while aligning with evolving legal and ethical standards.

Best Practice: Create cross-disciplinary teams to manage AI systems, which fosters accountability and continuous improvement.

Maintain a Defensible Audit Trail

In litigation, maintaining a defensible process is essential, requiring that AI-driven decisions are well-documented, with clear records of how AI was used, the data it processed, and the outcomes it produced. These decisions must also be reviewable, supported by logs that enable opposing parties and courts to understand and, if necessary, challenge the AI’s role in the process. Furthermore, all decisions, whether human- or AI-driven, should be justifiable, with a clear rationale that can be explained and substantiated.

Best Practice: Use tools that generate detailed audit trails and engage third-party validation if needed.

Innovate with Purpose

While leveraging AI for efficiency, it’s important to focus on innovation that aligns with strategic goals, such as reducing review time and cost, improving the accuracy of relevance and privilege determinations, and enhancing data protection and compliance capabilities.

Best Practice: Regularly assess whether AI investments are achieving the intended goals without compromising ethics, defensibility, or data security.

Conclusion

There’s no “Easy Button” when it comes to the use of generative AI – humans are still vital to the success of GenAI technology. By integrating AI with a strong ethical framework, robust human oversight, and a focus on defensibility, organizations can leverage technology to drive innovation while minimizing risks and safeguarding sensitive data.

In our next post in the series, we will discuss how to apply strategic approaches across multiple cases, class-action lawsuits and MDL litigations!

For more regarding Cimplifi eDiscovery, litigation, and investigations services, click here.

In case you missed any part of this series on complex litigation you can catch up on the entire series below.

Chess, Not Checkers: Strategic Approaches to Complex Litigation

• Key Elements of Effective Litigation Management
• Early Case Assessment and Strategic Planning
• Data Collection Strategies in Complex Litigation
• Navigating eDiscovery with AI and Tech Solutions
• The Human Element
• Coming Soon: Multi-Case Strategies
• Coming Soon: Negotiating from a Position of Power
• Coming Soon: Future Trends in Litigation Technology