Child welfare predictive risk models and legal decision making

Child welfare agencies around the world have experimented with algorithmic predictive modeling as a method to assist in decision making regarding foster child risk, removal and placement.

Thus far, all of the predictive risk models have been confined to the employees of the various child welfare agencies at the early removal stages and none have been used by attorneys in legal arguments or by judges in making child welfare legal decisions. This study will show the effects of a predictive model on legal decision making within a child welfare context.

Lawyers, judges and law students with experience in child welfare or juvenile law were recruited to take an online randomized vignette survey.

The survey consisted of two vignettes describing complex foster child removal and placement legal decisions where participants were exposed to one of three randomized predictive risk model scores. They were then asked follow up questions regarding their decisions to see if the risk models changed their answers.

Using structural equation modeling, high predictive model risk scores showed consistent ability to change legal decisions about removal and placement across both vignettes. Medium and low scores, though less consistent, also significantly influenced legal decision making.

Child welfare legal decision making can be affected by the use of a predictive risk model, which has implications for the development and use of these models as well as legal education for attorneys and judges in the field.