Efforts to counter human trafficking internationally must assess data from a variety of sources to determine where best to devote limited resources. These sources include the US Department of State’s Trafficking in Persons (TIP) reports, data from the Armed Conflict Location and Event Data project (ACLED), migration patterns, and social media. How can analysts effectively tap all the relevant data to best inform decisions to counter human trafficking?
Tom Sabo showcases a framework supporting artificial intelligence for exploring all data related to counter human trafficking initiatives internationally. The framework incorporates SAS rule-based and machine learning text analytics results not available in the original datasets, providing a depth of computer-generated insight for analysts to explore. As a focal point, Tom demonstrates how to apply rule-based text extraction of trafficking victims to generate training data for subsequent machine learning and deep learning models. Join in to learn how this framework provides decision makers with capabilities for countering human trafficking internationally and how it’s extensible as new AI techniques and sources of information become available.
Tom Sabo is a principal solutions architect at SAS. Since 2005, he has been immersed in the field of text analytics as it applies to federal government challenges. Tom presents work internationally on diverse topics including modeling applied to government procurement, best practices in social media analysis, and using analytics to leverage and predict research trends. He also served on a panel for the Institute of Medicine’s Standing Committee on Health Threats Resilience to inform DHS/OHA on social media strategies. He holds a bachelor’s degree in cognitive science and a master’s in computer science, both from the University of Virginia.
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