Human trafficking is a form of modern-day slavery that, while highly illegal, is more dangerous with the advancements of modern technology (such as the Internet), which allows such a practice to spread more easily and quickly all over the world. While the number of victims of human trafficking is large (according to non-profit organization Safe House, there are estimated to be about 20.5 million human trafficking victims, worldwide (“Human Trafficking Statistics & Facts.” Safe Horizon)- co-erced or manipulated by traffickers into either forced labor, or sexual exploitation and encounters), the number of heard cases is proportionally low- several thousand successful case prosecutions (Feehs K., p10-14). This disparaging fraction of unsettled human trafficking cases and trapped victims mandates that the system of fighting against human trafficking must be advanced.
This thesis presents an advancement of this field using a data pipeline that flows directly from law agencies and similar data-collecting groups to a web-based user-friendly interface that can be used for both research and analytical purposes and aims to allow legal-based efforts to proactively identify victims and traffickers as opposed to reacting to crimes after they happen. It displays data such as human trafficking case metadata (from title, to location, to verdict) and victim demographics (race, age, and sentence or conviction length, for example). This cleaned data is then stored and displayed through a Southern Methodist University-hosted infrastructure. vi Currently, only one source of data is curated, used, and stored, but this groundwork pipeline is built for expansion for a wide variety of sources- one projected source being PACER, (Public Access to Court Electronic Records). This expansive and flexible quality adds to the pipeline’s utility and projected future uses within the sphere of human trafficking discourse.
Dr. Sukumaran Nair
Computer Science, Engineering, General/Other
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Hites, Nathaniel, "Human Trafficking and Machine Learning: A Data Pipeline from Law Agencies to Research Groups" (2022). Computer Science and Engineering Theses and Dissertations. 25.