Classification with Explanation for Human Trafficking Networks

On a worldwide scale, an increasing number of victims of human trafficking were observed these last years, covering a majority of countries and territories. Among them, a large portion of women and girls are recruited primarily for sexual exploitation. United Nations Office on Drugs and Crime (UNODC) highlights the difficulties of access to justice which deprive victims of protection, a central issue behind our work. Our contribution is part of an emerging research trend, combining Artificial Intelligence (AI), Humanities and Social Sciences (HSS). It makes an original use of legal database to identify Human Trafficking Networks (HTNs), involving both sexual abuse victims and exploiters. First, a reformulation of the legal database as a numerical database is proposed, using new features expressing relationships between people involved in the same court case, likely to better reveal HTNs. Secondly, six machine learning algorithms, including Decision Tree, Random Forest, Gradient Boosting, Logistic Regression, Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) are used to train on numerical database and learn to classify the input court case into one of the three classes: Not suspicious, Suspicious, or Probably suspicious. We in details discuss knowledge-based feature engineering, dataset balancing, parameters tuning, and best models selection. The comparative empirical evaluations between those classification algorithms have been conducted in order to highlights the relevance of our HTNs detection approach. To help the end-users, to better understand the displayed HTNs, for Decision Tree and Random Forest, we also provide explanations of why such court case can be classified. Those results were finally discussed with experts in the field of human trafficking, providing us with interesting feedback shedding light to this multidimensional form of modern-day slavery problem.