  Web-based human trafficking activity has increased in recent years but it
remains sparsely dispersed among escort advertisements and difficult to
identify due to its often-latent nature. The use of intelligent systems to
detect trafficking can thus have a direct impact on investigative resource
allocation and decision-making, and, more broadly, help curb a widespread
social problem. Trafficking detection involves assigning a normalized score to
a set of escort advertisements crawled from the Web -- a higher score indicates
a greater risk of trafficking-related (involuntary) activities. In this paper,
we define and study the problem of trafficking detection and present a
trafficking detection pipeline architecture developed over three years of
research within the DARPA Memex program. Drawing on multi-institutional data,
systems, and experiences collected during this time, we also conduct post hoc
bias analyses and present a bias mitigation plan. Our findings show that, while
automatic trafficking detection is an important application of AI for social
good, it also provides cautionary lessons for deploying predictive machine
learning algorithms without appropriate de-biasing. This ultimately led to
integration of an interpretable solution into a search system that contains
over 100 million advertisements and is used by over 200 law enforcement
agencies to investigate leads.
