Child Maltreatment Hotline

Following the increased volume of child maltreatment cases during the Covid-19 pandemic, and in light of limited resources, a Special Committee seeks input from various stakeholders on the design of an Automated Decision System (ADS) using machine learning for identifying children at risk of maltreatment. The screening system will focus on calls placed to a hotline, where information of child maltreatment is disclosed to social-worker interns.

Based on the call and on additional information logged in the system or pulled from government and public databases, the system will automatically decide which calls to write off as not requiring any additional action, and which calls to flag as requiring an additional investigation to be performed by a certified social worker.

Ground Truth

In our data, 30% of the children are at risk a. Children at risk are those that would be removed from home by the child protection agency within two years from the call. The other 70% of them are not at risk b.

Model Predictions

In a perfect world, only calls regarding children at risk would be flagged as ones requiring an additional investigation, and only calls regarding children that are actually not at risk would be flagged as not requiring an additional investigation.

Model Mistakes

But models (and also humans) aren't perfect.

The model might make a mistake and not flag a child who is at risk c.

Or the opposite: the model might flag a child as one at risk f, while in fact they are not at risk and no additional investigation is required.

Never Miss Child at Risk...

One approach would be to have the model flag children at risk “aggressively”, such that close calls would be decided as “at risk”, and so the model will rarely miss a child at risk.

We can evaluate this model by counting the number of children who are correctly and incorrectly flagged as children at risk:

... Or Avoid Unnecessary Investigations?

On the other hand, certified social workers would be allocated to cases where no further investigation is required, resulting in wasting public resources and also in unnecessarily burdening families of children who are not truly at risk.

These issues and trade-offs in model optimization aren't new, but they're brought into focus when we have the ability to fine-tune exactly how aggressively the model works when flagging children as being at risk or not.

Improve Prediction Power

Sometimes it is useful to gather additional data to improve the predictive power of the model.

But do all data sources contribute equally? What are the meanings and consequences in terms of rights and values?

Previous child protection records
Family’s demographics records
Parents’ behavioral health records
Parents’ criminal records
Parents’ social benefits records
Parents’ employment records
Parents’ credit score records
Family members’ social media profiles

Putting it all Together

Now it is your turn to decide (1) which data sources to use and which to exclude; and (2) how aggressively the model is when flagging a call about a child at risk.

Previous child protection records
Family’s demographics records
Parents’ behavioral health records
Parents’ criminal records
Parents’ social benefits records
Parents’ employment records
Parents’ credit score records
Family members’ social media profiles

Try adjusting how aggressive the model is in flagging a call about child at risk