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Abstract:
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Efforts to counter human trafficking internationally must assess data from a variety of sources to determine where best to devote limited resources. These varied sources include the US Department of State’s Trafficking in Persons (TIP) reports, verified armed conflict events, migration patterns, and social media. This abstract is meant to address the question of how can analysts effectively tap all the relevant data to best inform decisions to counter human trafficking? Additionally, COVID-19 has presented a new challenge and has exacerbated trafficking situations and traffickers are exploiting the increased vulnerability of millions of people worldwide in forced labor or sex trafficking. We propose to show a framework supporting Artificial Intelligence on SAS® Viya® for integrating and exploring all data related to counter human trafficking initiatives internationally, incorporating the TIP, and ACLED sources as a starting point. The framework will include SAS rule-based and supervised machine learning text analytics results not available in the original data sets, providing a depth of computer-generated insight for analysts to explore. We will ultimately show how this extensible framework provides decision makers with capabilities for countering human trafficking internationally, and how it can be expanded as new techniques and sources of information become available such as Covid-19 data.
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