Throughout my career in eDiscovery, I've witnessed a profound transformation with the integration of artificial intelligence not only becoming a pivotal aspect of my work, but also playing a crucial role in the success of legal cases. At Array and within the industry, I find my purpose in bridging the gap between human expertise and AI capabilities, ensuring a collaborative synergy among people, processes, and technology.

My interest in AI stems from my background in computer science, coupled with a genuine curiosity about exploring and applying new tools to deliver the best service to clients. One particularly impactful tool that has brought significant accuracy and time savings to my clients is the use of Continuous Active Learning (CAL) workflows, which continuously analyze and categorize documents throughout the review process.

The exponential growth of data demands innovative approaches, and I am committed to guiding my clients on implementing CAL workflows in their eDiscovery. I implement CAL Workflows for my clients with several key steps:

1. Define Objectives and Parameters: I like to work with my clients to clearly outline their goals of the eDiscovery process and establish criteria for relevance. This foundational step ensures that the CAL system will align with the specific needs of the case.
2. Initial Training and Seed Set Creation: It is essential to begin with an initial training set (seed set) to kickstart the CAL workflow. This set should include a representative sample of documents, allowing the system to learn and adapt to the nuances of the case. The seed set acts as a richness sample, helps speed up the Active Learning process, and provides a starting metric to gauge the progress of the Active Learning project later. It's important to use high-value Subject Matter Experts (SMEs) to code the seed set, which does not need to be statistically relevant but should be a feasible number of documents for SMEs.
3. Continuous User Feedback: I work collaboratively with clients to ensure they provide feedback on document relevance throughout the review process. CAL relies on this continuous feedback loop to refine its understanding and improve accuracy. By actively engaging with the system, clients can help shape the AI's decision-making process and ensure more precise results. As more documents are coded, the model continually learns and updates its understanding of what makes a document responsive or not responsive.
4. Regular Monitoring and Quality Control: I implement regular monitoring and quality control measures to ensure the ongoing effectiveness of the CAL workflow. This involves periodic assessments of the system's performance and making adjustments as needed. By closely tracking key metrics such as precision, recall, and F1 score, I can identify areas for improvement and optimize the workflow accordingly. Validating the model is crucial for documentation and estimating the accuracy and completeness of the relevant document set. Validation provides statistics such as elusion rate, eluded documents, richness, recall, and precision

The impact of CAL workflows on eDiscovery cannot be overstated. In one notable project during my time at Acorn Legal Solutions (acquired by Array), we faced a massive 35 terabytes of data related to a large-scale overseas IP theft case. By deploying CAL workflows focusing on email threading, sentiment analysis, image detection, and prioritized review, we achieved remarkable results:

• Reduced review time by 60%, from an estimated 18 months to just 7 months
• Achieved a 95% reduction in false positives, minimizing unnecessary review
• Secured a $500 million damages award within a calendar year

This success story exemplifies the transformative power of CAL workflows in handling complex, data-intensive cases. However, implementing CAL is not without its challenges. Some common obstacles include:

• Ensuring data quality and consistency for optimal AI performance
• Overcoming resistance to change and fostering trust in AI-driven processes
• Continuously refining and updating the AI models to adapt to evolving case requirements

To navigate these challenges, I work closely with clients to develop tailored strategies, provide comprehensive training, and maintain open communication channels. By addressing concerns proactively and demonstrating the tangible benefits of CAL workflows, I help clients embrace this technology with confidence.

It's important to note that CAL is a process, not a magic button. As the saying goes, "AI will not replace lawyers. Lawyers using AI will replace lawyers not using AI." The goal is to create a synergy between human expertise and AI capabilities. While AI can identify which documents may be important, it cannot determine why they are important. That's where the human element comes into play.

Looking ahead, the future of CAL and AI in eDiscovery is filled with exciting possibilities. As AI continues to evolve, we can expect even more sophisticated tools for data analysis, pattern recognition, and predictive modeling. Legal professionals who stay ahead of the curve and adopt these cutting-edge technologies will be well-positioned to deliver exceptional results for their clients.

In the realm of eDiscovery, where the volume and complexity of digital information can be overwhelming, CAL workflows emerge as a beacon of efficiency, accuracy, and cost-effectiveness. By harnessing the power of AI and fostering a collaborative partnership between human expertise and machine intelligence, legal professionals can navigate the complex landscape of eDiscovery with confidence, ensuring a streamlined and defensible process that stands up to the scrutiny of the legal system.

At Array, we are committed to helping our clients leverage the power of AI and CAL workflows to maximize efficiency, minimize costs, and achieve optimal outcomes in their eDiscovery processes. If you're ready to explore how CAL and AI can revolutionize your eDiscovery strategy, reach out to our team of experts at Array. We are dedicated to providing cutting-edge solutions and personalized guidance to help you navigate the complexities of eDiscovery with confidence and precision. Let us be your trusted partner in harnessing the transformative potential of AI-driven eDiscovery.

Josh Treat, Director of Advanced Analytics

Recognized as a 2024 Relativity AI Visionary, Josh is a seasoned litigation support specialist with over 15 years of experience. He has saved millions in review costs by developing proprietary assisted review workflows across various platforms. Josh also employs quantitative forecasting and top-tier project management tools to ensure complex litigation reviews are completed on time and under budget. His unique skill set allows him to manage and navigate large, complex projects, offering creative, custom solutions that meet any budget or timeline requirements.