Fighting human trafficking with AI

Description

The growth of the web combined with the ease of sharing information it makes possible has led to increased illicit activity both on the Open and Dark Web, an egregious example being human trafficking. The DARPA MEMEX program, which funded research into domain-specific search, has collected hundreds of millions of online sex advertisements, a significant (but unknown) number of which are believed to be sex (and human) trafficking instances. At the same time, such data also provides an opportunity to study, investigate, and ultimately prosecute perpetrators of human trafficking by grouping and extracting patterns from millions of ads using automatic machine learning and natural language processing techniques.

Mayank Kejriwal discusses the development of a knowledge-centric architecture called Domain-specific Insight Graphs (DIG)—built under three years of MEMEX-funded research—that integrates cutting-edge AI techniques in a variety of fields. DIG reads and processes millions of ads from the web and places this information before investigators using a frontend interface. At the time of writing, DIG is being used by over 200 law enforcement agencies in the US for combating human trafficking and has led to actual prosecutions in both San Francisco and New York. DIG has also been extended in promising ways to combat other social problems like securities fraud and counterfeit electronics manufacturing.

Mayank offers an overview of DIG and explains how knowledge-centric architectures can help facilitate AI for social good. Along the way, he shares case studies on its successes and the key lessons learned during its development.