In the rapidly evolving field of legal technology, the courtroom defensibility of artificial intelligence (AI) within eDiscovery processes has emerged as a significant point of inquiry. Despite the growing attention towards the general defensibility of AI, discussions tend to narrow significantly when addressing its application in eDiscovery, and even more so regarding its utilization within the United States criminal justice system.
The concept of “defensibility” in a legal context refers to the ability of a practice or technology to withstand judicial scrutiny. This notion is crucial when integrating AI into eDiscovery, where the goal is to develop a discovery plan that efficiently identifies, collects, and produces relevant, non-privileged material from a vast array of electronically stored information (ESI). Such plans must not only be robust and cost-effective but also align with the Federal Rules of Civil Procedure, ensuring proportionality and transparency to the court and opposing parties.
Historically, the legal framework for assessing the defensibility of eDiscovery practices has been guided by principles outlined by United States Magistrate Judge Craig Shaffer in 2012. He proposed a four-part framework focusing on functionality, reasonableness, reliability, and understandability of eDiscovery protocols. These criteria aim to ensure that the methodologies employed for eDiscovery are adequately tailored to the specific needs of a case, offer a sensible balance between cost and litigation value, demonstrate reliable outcomes, and are comprehensible to all involved parties.
When considering AI in eDiscovery, it’s important to clarify that the term encompasses a wide range of technologies beyond Technology Assisted Review (TAR). AI in eDiscovery can include models trained for specific tasks like pattern recognition, anomaly detection for identifying unusual or biased communications, and tools for foreign language translation or image recognition. The application of AI extends the capabilities of legal professionals to manage and analyze data more efficiently and effectively.
The defensibility of using AI in eDiscovery does not hinge solely on prior judicial endorsement. Instead, it is based on the technology’s ability to meet the established criteria of functionality, reasonableness, reliability, and understandability. Legal professionals must ensure that the AI tools they employ are capable of achieving their objectives, are cost-effective, produce reliable outcomes, and can be understood by non-experts. The landmark decision by Judge Andrew J. Peck in 2012, which approved the use of TAR, underscored that the adoption of AI in eDiscovery should be justified by its practicality and reasonableness rather than a specific judicial seal of approval.
In conclusion, the integration of AI into eDiscovery processes presents a forward-thinking approach to managing the complexities of modern legal challenges. By adhering to established principles of defensibility, legal professionals can leverage AI technologies to enhance the efficiency and efficacy of eDiscovery while maintaining compliance with procedural norms. As the landscape of legal technology continues to evolve, the adaptability and understanding of AI’s role in eDiscovery will be paramount in navigating its defensibility in the courtroom.