Hound AI: Harnessing Generative AI to Combat Human Trafficking

Overview

Human trafficking is a global atrocity that affects millions of people each year, exploiting them for labor, sexual exploitation, and other forms of modern-day slavery. Despite significant efforts by law enforcement agencies worldwide, making a lasting impact on this heinous crime remains a formidable challenge. The complexity and clandestine nature of human trafficking networks make it exceptionally difficult to dismantle them effectively. However, targeting the logistics infrastructure of these criminal syndicates can significantly disrupt their operations. Enter Generative AI — a cutting-edge technology that holds the potential to revolutionize the fight against human trafficking.

The Challenge of Combating Human Trafficking

Law enforcement agencies face numerous obstacles in combating human trafficking. Traffickers are adept at evading detection, constantly changing their routes, modes of transportation, and places of operation. Victims, often traumatized and disoriented, provide testimonies that, while invaluable, can be fragmented and incomplete. This creates significant gaps in intelligence, making it hard to piece together the full picture of trafficking routes and operations.

Survivors’ testimonies are crucial, yet they frequently lack the specific details needed to pinpoint locations, vehicles, and other logistical elements critical for law enforcement. This is where the innovative application of Generative AI can make a substantial difference, offering two groundbreaking approaches to enhance the effectiveness of anti-trafficking efforts.

Approach 1: Generating Visual Clues from Testimonies

One of the most promising applications of Generative AI in this context is its ability to transform survivors’ fragmented memories into visual representations. Think of it as an AI powered sketch artist. When survivors recall certain aspects of their ordeal — such as a hotel room they stayed in, the appearance of a vehicle used in their transportation, or a distinctive landmark they passed — Generative AI can create detailed images based on these descriptions.

By generating photos of these crucial elements, law enforcement can gain clearer, more concrete leads. These images can be circulated among relevant agencies and the public, enhancing the chances of identifying and locating key points in trafficking networks. This approach leverages the power of AI to convert abstract, incomplete recollections into actionable intelligence.

Approach 2: Mapping and Analyzing Trafficking Routes

The second and the most crucial approach involves creating comprehensive metadata graphs of trafficking routes based on survivors’ testimonies. Generative AI can map out the routes taken, identifying stops and significant locations along the way. However, the true power of this approach lies in its ability to utilize Retrieval-Augmented Generation (RAG) to fill in the missing pieces of information.

RAG combines the generative capabilities of AI with large-scale data retrieval. It can search public databases, crowdsourced data, and other information repositories to uncover additional details that might not be immediately evident from testimonies alone. For instance, if a survivor mentions passing a specific type of building or landscape, RAG can search for matching structures or landmarks in the area described. This helps law enforcement to fill in the gaps, creating a more complete and accurate map of trafficking routes and operations.

Maximizing Law Enforcement Resources

By using Generative AI in these innovative ways, law enforcement agencies can make more efficient use of their limited resources. Targeting the logistics infrastructure of human trafficking networks allows for more strategic and focused operations. Instead of spreading resources thinly across vast areas with little actionable intelligence, agencies can concentrate their efforts on specific locations and routes identified through AI-generated insights.

This targeted approach increases the likelihood of successful interventions, leading to the dismantling of trafficking networks and the rescue of more victims. It also enhances the ability to prosecute traffickers by providing stronger, more detailed evidence.

Technical Implementation

The prototype of this solution was built using Meta’s LLaMA3 model as the foundation for the large language model. LLaMA3, a powerful and accurate LLM, is free and open-source from Meta. Its ability to run locally on-premises is particularly beneficial when dealing with sensitive information, such as law enforcement documents regarding human trafficking.

To implement the solution, LLaMA3 was integrated with LlamaIndex for Retrieval-Augmented Generation (RAG) and Groq for the inference endpoint. The use of LlamaIndex, backed by LLaMA3, allows the system to retrieve relevant information efficiently and augment the generative process with real-world data.

Imagine a graph that represents a real-life trafficking network. This graph consists of two types of nodes:

1. Known Logistical Nodes: These are locations and points of interest that law enforcement has identified as associated with the trafficking syndicate.
2. Unknown Logistical Nodes: These are locations and transportation modes that are not yet fully identified, but there are shards of information available about them.

LLaMA3 leverages the power of RAG systems like LlamaIndex to fill in the gaps of information. It can search real-world data to conclude which place might be the unknown node in question. For example, if a survivor describes a partial route with some known and some unknown nodes, LLaMA3 can use the available data to infer and identify the unknown nodes, completing the route and providing law enforcement with precise locations and logistics.

Example

For example a survivor was rescued at a dock yard. From their testimony, they are sure that they were at the city A bus stop. They traveled for 12 hours and then they were at a hotel that had red roses on the wallpaper. After that they were rescued at the dock yard.

The survivor remembers that there was a white lighthouse near the hotel. They also remember that the hotel had 2 swimming pools and a fountain. The rooms had a wallpaper that contained red roses.

Used manually, this information is hardly actionable. But AI can help search this information in publicly available or crowdsourced data sets and form a correlation. It can also perform an agentic search with information like the number of hours it took for them to reach the unknown node. A testimony could also contain information about the weather conditions or if there was a storm or heavy rain. Agentic RAG can search for relevant information by accessing the internet as well.

I created a fictitious hotel called hotel seaview and put its summary in a PDF file. The summary is like this:

“Perched on the edge of a pristine beach, the Seaview Hotel presents an idyllic retreat, combining luxurious amenities with natural beauty. The property welcomes guests with an ornate fountain at the entrance, its waters cascading elegantly, setting the tone for a serene stay. On either side of this centerpiece lie two inviting swimming pools, each offering a refreshing escape beneath the sun. Adding to the charm is a beautifully crafted statue of a woman fetching water, situated gracefully between the pools, symbolizing serenity and the seamless blend of art with nature. The rooms within the hotel are equally enchanting, adorned with romantic red roses wallpaper that echoes the vibrant hues of the nearby floral landscapes. Just a short stroll away, the beach unfolds into a stunning expanse of sand, leading to a striking white lighthouse that stands as a beacon of tranquility and navigation. Together, these elements create a perfect harmony of luxury, art, and natural allure at the Seaview Hotel.”

I used the following code as the backend for RAG:

# Import necessary libraries
import nest_asyncio
from fastapi import FastAPI
from pydantic import BaseModel
import uvicorn
# Import specific classes and models
from llama_index.llms.groq import Groq
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core import Settings
from llama_parse import LlamaParse
from llama_index.core import SimpleDirectoryReader
from llama_index.core import VectorStoreIndex
from fastapi.middleware.cors import CORSMiddleware

# Apply nest_asyncio to enable running asyncio in a notebook or standard python environment
nest_asyncio.apply()

# FastAPI app setup
app = FastAPI()

# CORS configuration
origins = [
"http://localhost:3000", # Allow your local React app
"https://yourproductiondomain.com" # Also allow your production domain if needed
]

app.add_middleware(
CORSMiddleware,
allow_origins=origins, # Allows specified origins (websites) to make requests
allow_credentials=True,
allow_methods=["*"], # Allows all methods
allow_headers=["*"], # Allows all headers
)

# Initialize LLM and embedding models
llm = Groq(model="llama3-8b-8192")
llm_70b = Groq(model="llama3-70b-8192")
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")

# Set global settings for LLM and embedding models
Settings.llm = llm
Settings.embed_model = embed_model

# Load documents (assuming these operations are synchronous)
docs_seaview = SimpleDirectoryReader(input_files=["./Seaview.pdf"]).load_data()
index = VectorStoreIndex.from_documents(docs_seaview)
query_engine = index.as_query_engine(similarity_top_k=3)
# docs_seaview = LlamaParse(result_type="text").load_data("./Seaview.pdf")

# This will need a key from llamaindex cloud

# Request model
class Query(BaseModel):
question: str

# Endpoint to process the question
@app.post("/ask/")
async def ask_question(query: Query):
# response = llm.complete(query.question)
response = query_engine.query(query.question)
return {"response": str(response)}

# Run the server using uvicorn if this script is executed directly
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=8000)

Rightly so, the system was able to confirm that the unknown node is indeed the fictitious Hotel Seaview.

Conclusion

The integration of Generative AI into the fight against human trafficking represents a significant leap forward in the capabilities of law enforcement agencies. By transforming fragmented memories into visual clues and creating detailed maps of trafficking routes, AI can bridge the gaps in intelligence that have long hindered anti-trafficking efforts. With this technology, law enforcement can disrupt the logistics infrastructure of trafficking networks, making a profound impact on this global crisis. As we continue to innovate and refine these approaches, the hope is that we can bring an end to the suffering caused by human trafficking and ensure a safer world for all.

About the Author

Shashank Ashtikar is the Founder and CEO of Momentum AI and is very passionate about the usage of AI for social good. Please reach out to him with any ideas or feedback related to Hound at shashank@momentumai.dev.