Intervention(s)
The proposed intervention Bússola I.A. aims to use state-of-the-art machine learning for the task of
processing incoming labor trafficking tips and complaints, automating and expediting processes that
were previously conducted manually. Figure 1 illustrates the theory of change, in which features of
Bússola I.A. address key bottlenecks hindering optimal anti-trafficking response, leading to process
improvements that have direct implications for MPT outputs and trafficking related outcomes. The
tool will deliver contextually relevant information to labor prosecutors to help prioritize hundreds of
pending trafficking tips and swiftly identify the presence of pertinent information in an incoming tip,
thus facilitating informed decision-making.
Bússola I.A. is a modular tool with features that fall into three categories. First, Bússola I.A. performs
near real time risk scoring to identify and prioritize the tips and cases most likely to lead to trafficking
discovery. To do so, the model uses a nimble, locally implemented open source large language
model to process the information contained in the incoming tip, which is submitted in free text
narrative format. The tool organizes extracts key features from tip received that are relevant for
decision making, organizing these features into a structured dataset (for example: whether violence is
reported, whether children are mentioned, whether an address is provided, economic sector). These
features are then used in a machine learning model to estimate the likelihood of trafficking discovery and the priority level of the tip. This method aims to reduce reliance on subjective intuitions or
availability-bias in the early stages of case prioritization, leading to more effective case prioritization
and improved task force targeting.
Second, Bússola I.A. uses a locally implemented large language model to produce a concise text-
summary of the key features of the tip, along with the primary components driving the tip’s risk score.
The goal of the LLM-generated summaries is to provide a quick, high-level summary to prosecutors,
and to describe the main components driving the risk score corresponding to the tip. These features
are critical for quickly conveying key pieces of information in an easy-to-understand and interpret
narrative framework, as well as explaining the risk score prioritization. The LLM augmentation
is designed to expedite tip processing for faster desk investigations and more timely task force
responses.
Third, Bússola I.A. visualizes the spatial distribution of all active tips in an interactive map showing
the locations of each active case. This feature allows prosecutors examining a given case to view all
cases originating from nearby locations and facilitates the identification of clusters of tips, leading to
larger task forces which investigate more cases.
The tool places a strong emphasis on interpretability and transparency allowing for real-time interro-
gations of model outputs to build trust with human decision makers. Moreover, the Bússola I.A. tool
is designed to complement and integrate into the existing case management system MPT Digital as a
dashboard extension, minimizing changes in workflow required to access Bússola I.A. analytics. In
other words, Bússola I.A. is designed to be a new feature of the existing MPT Digital platform rather
than a completely new replacement.