Can big data save the next Megan Khung? It’s definitely worth a try to keep children safer

SINGAPORE – Can big data predict the risk of abuse before it happens and alert child protection officers to save children who may be suffering behind closed doors?

The Ministry of Social and Family Development (MSF) is venturing that it can.

On September 22, the MSF launched its SAFER (Sensemaking Alerts for Frontline Engagement and Response) unit, to pull together information from different agencies to identify cases that need more attention. This will hopefully prevent another tragic death like Megan Khung’s.

Megan’s mother and her ex-partner horrifically abused the four-year-old girl to death, and the couple burnt her corpse to avoid detection.

Had SAFER been in place then, her case would have been flagged for review about five months before her death, MSF said.

How risk algorithms can help tackle child abuse

SAFER relies on a risk algorithm based on data analytics to combine information from multiple government agencies - such as the Central Narcotics Bureau, police and MSF agencies - for families with young children with child protection concerns, such as abuse or neglect. 

Singapore is not the first country to use risk algorithms in child protection.

In 2016, the authorities in Allegheny County, Pennsylvania, introduced the Allegheny Family Screening Tool to help staff decide whether to investigate cases of potential child abuse.

The tool considers over 100 criteria, such as jail records, psychiatric services and public welfare benefits. A 2019 evaluation found that the tool “moderately” improved decisions to investigate cases.

In Singapore, SAFER is not meant to be a magic bullet to stem out child abuse. By analysing indicators such as pre-school attendance and withdrawals, drug and sexual offences, SAFER builds a risk profile for these children and their families. 

What it can do is to monitor and flag developments among the thousands of families here that social service professionals are currently working with for child protection concerns.

In 2024 alone, there were 2,303 new high-risk child abuse cases, and 3,292 new lower risk cases. 

But the algorithm does not replace human judgment.

Child protection staff will review potential concerns flagged by SAFER, and use their professional judgment to decide on the next course of action. 

Such action could involve coming up with a plan to keep the child safe for new cases, or re-looking existing safety plans if new developments in the family raise concerns. 

More importantly, SAFER could keep watch over the tens of thousands of child protection cases that have been closed and where active intervention has stopped. 

Many of these families struggle with multiple complex problems, such as poverty, drug or crime issues, mental health conditions and relationship conflicts. Even after a case is closed, stresses within families could flare up again, potentially tipping the child back into harm’s way.  

Connecting these disparate pieces of data to form a fuller picture of what is going on at home is critical in coming up with stronger safety plans for the child, social workers say.  

For one thing, families may hide certain information or may even lie - perhaps out of shame and embarrassment or simply because some experiences are too painful to talk about.

However, the risk assessments and subsequent safety plans may look very different once the protection professionals know, for example, that a parent is abusing drugs or if one parent has a history of spousal violence.

In Megan’s case, her mother and her ex-partner were on drugs, and the mother lied about Megan’s injuries when the girl’s pre-school teacher asked.

Coupled with multiple breaches by various agencies, such as police officers not following protocols and an officer from the Child Protective Service who failed to log a call by Megan’s pre-school seeking help, the system failed to save Megan.

Potential pitfalls of using data

Despite its promises, risk algorithm models also come with potential pitfalls.

For one thing, how robust the model is depends on the quality, scope and timeliness of the data it has access to. 

The Allegheny Family Screening Tool, for example, has been criticised for disproportionately flagging low-income families because its databases have far more data on poor families than richer ones.

This creates a risk of poorer families being subject to greater scrutiny, while missing out on detecting abuse in wealthier households.

Another question is how the parameters for alerts are set.

Setting thresholds for alerts too strictly risks generating many false alarms, which may add to the workload of social service professionals. This may also cause more tension among families who could be subjected to repeated checks and questioning.

Ultimately, SAFER’s success will depend on regular auditing and continuous fine-tuning to ensure its model stays sharp.  

The new unit is just one part of MSF’s strengthened child protection framework.

In the past year, the MSF rolled out a series of measures to boost the child protection system following recommendations made by the review panel on Megan’s case.

The MSF has formed an independent panel of experts to resolve disagreements between agencies over how best to protect a child, designated agencies with the relevant expertise to manage child abuse cases, and set up a $15 million care fund for well-being initiatives for those involved in protection work.

Together, the measures point to a decisive shift towards a more proactive, co-ordinated and robust child protection system. 

Technology has its limits. While algorithms can process vast amounts of data at a speed and scale no human can match, at the end of the day, they do not save children. Humans do. 

Turning the alerts into actions still depends on people - such as watchful teachers and people around a child who notice something is wrong and alert the authorities, and social service professionals who can assess the risks and intervene.

It takes a collective resolve, and not just one SAFER unit, to ensure that no child meets the same fate as Megan again.