Abstract

Human trafficking, affecting over 50 million people globally, is a complex criminal enterprise in which traffickers actively conceal and distribute information across fragmented and often illicit online platforms. Traditional investigative tools are ill-suited for detecting patterns across such obfuscated, heterogeneous data. This paper presents Domain-specific Insight Graphs (DIG), an investigative AI search engine designed to operate at web scale and enable non-technical decision-makers, such as law enforcement and prosecutors, to rapidly uncover actionable leads in human trafficking investigations. DIG employs a novel AI pipeline that ingests large, diverse web corpora (including trafficking-relevant advertisements), cleans and normalizes extracted information, and links entities into a semantic knowledge graph. A domain-optimized search layer allows investigators to traverse these graphs to identify potential victims, perpetrators, and trafficking networks. Unlike commercial alternatives, DIG was released free of charge, open-sourced, and deployed to over 200 U.S. state and local law enforcement agencies through the DARPA Memex program. Deployment results demonstrate measurable impact: in New York, agencies using DIG reported a drop in sex worker arrests and an increase in trafficking-related arrests from <1% to over 60%, disrupting cycles of victim re-victimization. The system has been credited in high-profile prosecutions and received endorsements from District Attorneys. This paper details the problem context, AI approach, deployment process, operational challenges, and lessons learned from maintaining DIG post-federal funding …

Date

2026

Authors

Mayank Kejriwal

Journal

Proceedings of the AAAI Conference on Artificial Intelligence

Volume

40

Issue

47

Pages

40015-40024