Active Search of Connections for Case Building and Combating Human Trafficking

Abstract

How can we help an investigator to efficiently connect the dots and uncover the network of individuals involved in a criminal activity based on the evidence of their connections, such as visiting the same address, or transacting with the same bank account? We formulate this problem as Active Search of Connections, which finds target entities that share evidence of different types with a given lead, where their relevance to the case is queried interactively from the investigator. We present RedThread, an efficient solution for inferring related and relevant nodes while incorporating the user's feedback to guide the inference. Our experiments focus on case building for combating human trafficking, where the investigator follows leads to expose organized activities, i.e. different escort advertisements that are connected and possibly orchestrated. RedThread is a local algorithm and enables online case building when mining millions of ads posted in one of the largest classified advertising websites. The results of RedThread are interpretable, as they explain how the results are connected to the initial lead. We experimentally show that RedThread learns the importance of the different types and different pieces of evidence, while the former could be transferred between cases.

Network-theoretic modeling of complex activity using UK online sex advertisements

“…Systems that can assists NGOs and law enforcement exist, of course; for example, in our group, we built the DIG (Domain-specific Insight Graphs) system (Szekely et al 2015) to help law enforcement selectively ferret out ads that are indicative of human trafficking. Other systems include DeepDive and FlagIt, and use a combination of database and Artificial Intelligence technologies Rabbany et al 2018;Alvari et al 2017;Burbano and Hernandez-Alvarez 2017). For example, Rabbany et al (2018) explore methods for active search of connections in order to build cases and combat human trafficking.…”

“…Other systems include DeepDive and FlagIt, and use a combination of database and Artificial Intelligence technologies Rabbany et al 2018;Alvari et al 2017;Burbano and Hernandez-Alvarez 2017). For example, Rabbany et al (2018) explore methods for active search of connections in order to build cases and combat human trafficking. Similarly, FlagIt attempts to semi-automatically mine indicators of human trafficking (which include movement, advertisement of multiple girls etc.)…”

Network-theoretic information extraction quality assessment in the human trafficking domain

“…More broadly, semi-supervised and minimally supervised AI has been applied to fight human trafficking in contexts beyond information extraction and search (Alvari et al 2017;Burbano and Hernandez-Alvarez 2017;Kejriwal et al 2017;Rabbany et al 2018). As one example, the FlagIt system, recently developed in our group, attempts to semi-automatically mine indicators of human trafficking (which include movement, advertisement of multiple girls etc.)…”

Character-Based Models for Adversarial Phone Extraction: Preventing Human Sex Trafficking

“…Most of these use phone numbers as features, and several found them to be among the most important input (Dubrawski et al, 2015;Nagpal et al, 2017;Li et al, 2018). In fact, phone numbers are used as gold truth to connect similar ads or link traffickers (Rabbany et al, 2018;Li et al, 2018). Phone numbers have also been shown to be some of the most stable links to entities (Costin et al, 2013), so are important for entity linking tasks.…”