Leveraging Machine Learning for Early Case Assessment

Presenters: Lanny Morrow & Jeremy Clopton
CPE Credit: One CPE credit in Specialized Knowledge and Applications field of study may be awarded
upon verification of participant attendance.

Legal discovery is one of the costliest aspects of litigation. Early case assessment (ECA) is designed to estimate the risks and costs of pursuing or defending a legal case and could save the parties significant time and money if properly implemented. The ECA process often involves analyzing a massive volume of textual content, such as documents, email and social media. This complimentary webinar will explore the digital forensics and machine learning tools that can significantly speed up the ECA process by mapping out relationships between parties, locating relevant content in documents and identifying and quantifying types of files and areas where evidence could exist.

Upon completion of this webinar, participants will be able to:
• List data sources relevant in the ECA process and understand how digital forensics can help identify, quantify and preserve them
• Identify less obvious sources of relevant data, such as cloud-based accounting systems, distributed data storage and social media, and how they can affect ECA
• Discuss how machine learning (teachable) software can dramatically reduce time and costs during ECA discovery
• Describe how machine learning tools can help detect obscure relationships between parties and emotional tones in communications and help perform rapid, efficient “find more like this” searches in vast document collections
• Explain how tying these concepts together can give them a competitive advantage in their ECA process that’s not available through traditional methods