Technology and data unite every aspect of our lives, and the digital information that we produce is being put to new uses every day. Predictive analytics are used by major card companies to detect fraud, while marketers use data to make campaigns more effective and determine who their target audiences are. With everyone leaving such distinct digital trails, analysis of these digital paper trails is being put to use to improve the lives of children.
Big data is starting to be put to use in support of social workers’ goals of providing help and support to those in need, and in volatile situations. Analytics uses collected and new data to identify patterns, predict behaviors, and help focus the resources of the child welfare system where they can do the most good. But how are they doing it?
Organizations across the world use predictive analytics, to the point that it is vital to the success of any business. Predictive analytics takes a mass of information from multiple sources and runs it through computer systems to sort, analyze, and process. Then, when new information is added to the system, we are able to compare it to previous information.
These systems use what we already know about similar data, and project the most likely path for new data. Credit card companies use this method to detect fraud, and big data is now being used to amass information regarding child behavior, living situation, reports, and investigations to predict the risk of future and recurring abuse.
Approximately seven million children are brought to the attention of child welfare services annually, and new reports are constantly circulated in the media about investigations into the deaths of children due to abuse. The use of predictive analytics is being applied to help aid call screeners in determining if a case file should be opened. Previously this decision was left up to the judgment of call screeners, but the new process can help screeners and their superiors make more informed decisions.
After receiving a phone call, screeners take the necessary details and input them and the child’s identity into their system. The program then determines a risk score between one and 20. The closer to 20 the child’s score is, the more likely they are to be in an unsafe situation. The analytics consider the parents’ prior offenses, reports that have been made, and many other points of related information about the child and their relationship to the people in question.
Ultimately the decision of whether or not to open a case is made by screeners in collaboration with supervisors. Los Angeles is already implementing a version of this system to help make more informed decisions, and while it is not a silver bullet to fix an admittedly messy system, it is helping to focus resources. These benefits are not without their potential downfalls, however.
Concerns About Privacy
As is always the case with the uses of Big Data, there are concerns about how these systems may be implicitly biased, and the information misused. Without building in context, disenfranchised families and communities that already struggle with the legal system could be left open to unwarranted scrutiny while leaving higher-income, white perpetrators unidentified. This is one of the core reasons that the implementation of big data is intended to be a tool, not a cure-all.
Given that the legal definition of abuse is open to a certain amount of interpretation, analytics systems can’t be relied on entirely to make determinations, and require the perspective of a social worker to be useful. Though spanking and corporal punishment are legally allowed in most states, when this escalates to the point that bruises are left and physical damage is done, it crosses the line from discipline to abuse. While analytics systems may be able to see medical, school, or police reports that indicate these factors, it is up to social workers to make the final determination.
Data is especially important when considering the type of abuse that can occur over the internet. While children need to be able to navigate technology for increasingly digital classrooms, careers, and communication, the prevalence of technology can leave them open to predators both inside and outside the home.
The accumulation of data from cell phones, laptops, and wearable technology can tell social workers important information. This includes verifying location, whether or not tracking has been on and accessed on various devices, and even medical information recorded through smartphones and health trackers.
Similar to evaluating the likelihood of child abuse, big data is being put to work to identify and stop human sex trafficking. A major problem in apprehending and prosecuting those who recruit and are responsible for trafficking is finding ways to tie their data to individuals and geographic locations.
Big data has been successful in helping investigators identify how sex trafficking networks recruit their victims and it can help them identify neighborhoods and cities where this type of advertising is present. Authorities are then able to focus on raising awareness, monitoring, and investigating the highest risk areas.
These systems are combined in the identification of child victims of sex-trafficking. With almost a quarter of those who are trafficked being children, the ability to analyze reported behavioral changes, controlling relationships, and other red flags can be instrumental in identifying and protecting children in high-risk situations. Information regarding their relationships, in particular, can help the systems identify children in danger of being trafficked, and recover those who have already fallen victim to trafficking.
As these systems are shown to be effective, we are able to collaborate with survivors on building better systems. By talking with survivors of sex trafficking and abuse, more information is added to these analyses, making them more accurate and useful.
In the case of abuse, victim reports add more information about behaviors, relationships, and signs of abuse. Similarly, for sex trafficking identification, we can learn more about what makes certain areas high-risk, identify where to focus education efforts, and close the gap between social workers and those who need them most.
Big data is in every part of our lives, from medical coding to records of lunch orders. This proliferation of data is beginning to find uses outside of identifying credit card fraud and predicting market fluctuations. As we refine our digital tools for identifying at-risk children and adults, we are building more effective w