Hurricane Score
ICEProblem Statement
This use case intends to solve the problem of understanding which noncitizens in Enforcement and Removal Operations’ Alternatives to Detention - Intensive Supervision Appearance Program are most likely to abscond.
Expected Benefits
The Hurricane Score helps officers quickly evaluate substantial amounts of case information across thousands of Alternatives to Detention - Intensive Supervision Appearance Program participants. By surfacing a risk indicator based on observed absconding patterns, it can provide additional insight that might not be apparent from manual review alone. This supports more consistent and efficient case reviews and helps officers allocate case management resources more effectively while maintaining individualized assessments.
System Outputs
Once individuals are enrolled in Enforcement and Removal Operations’ Alternatives to Detention - Intensive Supervision Appearance Program (ATD-ISAP), officers periodically review each case to determine whether the current level of case management and technology assignment remains appropriate or should be adjusted. During case reviews, an analyst or officer provides the Hurricane Score model with information already known about an ATD-ISAP participant, including case management details and participant actions. The model is a quasi-binomial, binary classification machine learning (ML) model trained on inactive ATD-ISAP case data to identify patterns associated with prior absconding behavior. Based on the provided inputs, the model outputs a score from 1 to 5, with higher scores indicating a higher model-estimated risk that the individual may abscond. Officers may then consider this score, along with many other factors, when determining whether current levels of case management or technology assignment remain appropriate or should be adjusted.
Documentation
Data & Code
Links
Risk Management
Potential Impacts
Predictive ML techniques can produce misleading results, such as false positives, which could impact case management decisions if relied upon as a primary factor. For instance, an inaccurate hurricane score might lead to stricter or more lenient compliance or technology requirements for an individual. ERO mitigates this by using the score as one of many factors in determining case management or technology levels for individuals in the ATD program.