There is no good way to begin a conversation about child abuse or neglect. Data science is a sad and oftentimes sickening topic. But the fact of the matter is it exists in our world today and frequently goes unnoticed or unreported, leaving many children and young adults to suffer. Data Science Training in Chennai Of the nearly 3.6 million events that do get reported, there are rarely enough resources to go around for thorough follow up investigations.
This means that at least some objective decisions have to be made by professionals in data science field. These individuals must assess reports, review, and ultimately decide which cases to prioritize for investigation on a higher level, which ones are probably nothing, and which ones are worrisome but don’t quite meet the definition of abuse or neglect.
Overall, it can be a challenging job that wears on a person, and one that many think is highly based on subjective information and bias. Because of this, numerous data researchers have worked to develop risk assessment models that can help these professionals discover hidden patterns and/or biases and make more informed decisions Data Science Training in Chennai.
Defining the Work Space
Defining exactly data science what falls under the category of child abuse or neglect can be a surprisingly sticky topic. Broadly, it means anything that causes lasting physical or mental harm to children and young adults or negligence that could potentially harm or threaten a child’s wellbeing. What exactly constitutes child abuse can depend upon the state you live in.
Ultimately, a lot of the defining aspects are gray. Does spanking count as physical abuse or is the line drawn when it becomes hitting with a closed fist? Likewise, are parents negligent if they must leave their kids home alone to go to work? Does living in poverty automatically make people bad parents because there may not always be enough food in the house?
Sadly, many of these questions have become more prevalent during the COVID-19 pandemic. With many families cooped up at home together, not going to work or to school, kids who live in violent households are more likely to be abused and fewer people are seeing the children regularly to observe and report signs of abuse.
Unfortunately, limited statistical data is available at this point, but with so many people having lost jobs, especially amongst families that may have already been teetering on the edge of poverty, situations that could be defined as neglectful are thought to be exploding in prevalence.
Identifying Patterns
The idea of using data science to help determine the risk of abuse and neglect that many children face can be seen by many as a powerful means of tackling a difficult issue. Much like many other aspects of our world today — data has become a very useful and highly valued commodity that can work to help us understand some of the deeper or hidden patterns.
That is exactly what has been incorporated in Allegheny County, Pennsylvania. The data science algorithm that was developed assesses the “risk factor” for each maltreatment allegation that is made in the county. The system takes into account several factors including mental health and drug treatment services, criminal histories, past calls, and more. All of this ultimately adds up to helping employees take into account how at-risk a child may actually be and whether or not the case will be prioritized for further investigation. Generalized reports indicate that the program works well, but that even it can ‘learn’ to make decisions based on bias.
In this situation, the goal of the program isn’t to take all of the power away from the employees but rather to work as a tool to help them make a sounder decision. Some risk factors will automatically be referred to a case handler for further investigation, but most will allow for the case assessor to weigh the algorithm with research and other information that may not be well accounted for in the model. If there are significant differences between the case assessor’s conclusion and the model’s conclusion, a supervisor reviews the information and makes the final decision.
Dealing with Bias
Of course, data science using a model such as this one can be a double-edged sword. Certain things are difficult to account for. For instance, if parents take financial advantage of their children by using their clean Social Secure Numbers to open credit cards and other credit accounts, their children can then be saddled with poor credit they did not create. Because this type of financial abuse is difficult to prove, it can be difficult for young adults to repair their credit later in life or hold their parents or guardians responsible.
And the algorithm may struggle to recognize this as abuse. But it can also take out some of the bias that many of the call takers can inadvertently have when they are assessing the risk level of certain cases. A difference in conclusion from the model can force them to take a second look at the hard facts.