As state and local government leaders and community advocates explore how predictive analytics can improve child well-being outcomes, many questions and potential concerns surface.
It is critical to understand that government youth services agencies across the United States have used actuarial Risk & Need Assessment tools for many years. Many of these were developed using predictive analytics to identify “static” risk factors, such as low birth weight and mother was previously in foster care, that contribute to risk of maltreatment.
While these tools are a good starting point, there face implementation problems such as accuracy requirements, worker bias, amount of time to complete, etc. With an operational analytic approach, analytics is continually assessing and reassessing data, making the system smarter. Ongoing assessment of risk and needs allows for both static and dynamic factors to be used when calculating risk of maltreatment. This supports increased accuracy, real time risk scoring, limited worker bias, etc.
Below is a comparison/contrast of actuarial tools vs. and operational analytic solution such as SAS for Child Safety:
I worked in child well-being for 22 years, and have met with countless leaders and community advocates to discuss advance analytic approaches and their applicability to human services. Several concerns have been raised consistently that can be addressed with data driven and research based responses. These include:
- False Positives
- Racial Bias/Disparity
- Equation vs. Comprehensive Process
- Current Tools are Sufficient
In my next posts, I will dive deeper into these concerns as well as findings from my peers in SAS Solutions on Demand, who are engaged in pioneering work on child welfare analytics. I will explain why leaders and child well-being advocates can feel confident in predictive analytics, and its ability to help kids and ease the burden on caseworkers.
Do you have concerns beyond these, or heard of others? Please share in the comments.