The following outputs are a result of the collaboration between Tech Against Trafficking members, partners, and the organizations participating in the TAT Accelerator, and they are intended to benefit the broader anti-trafficking field. They have been developed during or following one of TAT’s Accelerator programs in partnership with the participating anti-trafficking organizations.

2. Privacy-Preserving Mechanisms for Human Trafficking Data

Challenge: Understanding the scale and scope of human trafficking is essential for effective resource allocation, technology application, and policy development. However, one of the critical challenges of the anti-trafficking community is how to share victim data for analysis while protecting the privacy and safety of the individual victims represented in that data.

Solution: Privacy-preserving mechanisms such as synthetic data may offer a solution, and TAT has worked to make this technology more accessible to the anti-trafficking community. The solution uses privacy-aware data sampling to generate a synthetic dataset that represents the statistical properties of the sensitive dataset rather than actual (potentially identifiable) individuals, augments the synthetic data with precomputed aggregates for comparison and official statistical reporting, and creates data interfaces that allow the user to explore both datasets in parallel.

Resources: The following resources can be used by organizations seeking to analyze human trafficking data in a way that preserves the privacy of victims.

Synthetic Data Showcase tool:

• Background: Design of a Privacy-Preserving Data Platform for Collaboration Against Human Trafficking | Microsoft Research
• GitHub Project: microsoft/synthetic-data-showmicase
• Public Utility web app: https://microsoft.github.io/synthetic-data-showcase/

3. Data Standards for Victim Case Management

Challenge: Recent technological advances have enabled a growing number of organizations worldwide to develop cost-effective victim case management systems and services. However, organizations frequently use different terminology and criteria when assessing and inputting victim information, which makes it difficult to share case data among the anti-trafficking community, conduct meaningful analysis, and understand the scale of the problem.

Solution: To address this challenge, TAT developed a data standard in collaboration with the Counter-Trafficking Data Collaborative (IOM). The Human Trafficking Case Data Standard (HTCDS) establishes common criteria and language that can be used across organizations managing victim case data.

Resources: The following resources can be used by organizations looking to develop victim case management systems and those seeking to share datasets and analyze trends.

Human Trafficking Case Data Standard:

• IOM Announcement: Strengthening the Evidence Base on Trafficking in Persons
• GitHub Project: UNMigration/HTCDS

4. Causal Tools for Evidence-based Policy

Challenge: To address the root causes of human trafficking, anti-trafficking organizations are advocating for policy changes. However, it is challenging to make strategic policy decisions without evidence that shows the real-world causes driving a phenomenon, and not simply correlations. Similarly, it is hard to evaluate the causal impact of enacted policies in the presence of other real-world factors that may vary over time.

Solution: TAT provided representative problems, data, stakeholders, and questions used to inform the design of “ShowWhy”, a technology that supports discovery and estimation of causal effects in observational data (e.g., the effects of real-world risk factors, interventions, policies, or campaigns). The tool assumes no prior knowledge of coding or causal inference and explicitly targets use by domain experts in policy contexts.

Resources: The following resources can be used by anti-trafficking organizations looking to elevate the level of data-driven evidence used for advocacy efforts.

ShowWhy tool:

• GitHub Project: microsoft/showwhy
• YouTube Video: Introduction to ShowWhy, user interfaces for causal decision-making
• Background: Real-world evidence and the path from data to impact | Microsoft Research