A one-day interdisciplinary design sprint and competition at Texas State University to create an application for law enforcement to tackle human trafficking. The goal of this project was to combine existing software and integrate new ideas into one application. Judges were Jessica Mast, program specialist from the Trafficking and Transnational/Organized Crime Division in the Texas Office of the Attorney General; Nathaniel Haefner, design and photography teacher at Austin ISD; and Cameo Chattin, a senior UX designer for Home Depot.
Attempt to end the atrocity of human trafficking through a new application, which enables law enforcement agencies to accurately auto-scrape the internet for escort services, ads, keywords and locations. Additionally, the objective is to implement facial recognition and conceptualize a self-learning algorithm to save and share data within government agencies.
Because this application is strictly for law enforcement, it must be password protected and minimal, making it easily navigable for the agent. Because the code words within human trafficking are ever-changing, it was important to create the ability of submitting new found codes. Once they are submitted to Canary, the system automatically highlights every new code within all posts. It was also critical to implement features like facial recognition and tracking so the agents can see all connections to the potential victim. For a human trafficking victim, it is extremely difficult to get out of “the life.” Therefore, the logo is a combination of a maze, an eye, and a camera lens.
The proposed project was chosen among five other teams to be actualized and implemented into law enforcement.
