A Practical Framework for Considering the Use of Predictive Risk Modeling in Child Welfare

Date01 November 2020
Published date01 November 2020
162 ANNALS, AAPSS, 692, November 2020
DOI: 10.1177/0002716220978200
A Practical
Framework for
Considering the
Use of
Predictive Risk
Modeling in
Child Welfare
978200ANN The Annals Of The American AcademyPredictive Risk Modeling In Child Welfare
Predictive risk modeling (PRM) is a new approach to
data analysis that can be used to help identify risks of
abuse and maltreatment among children. Several child
welfare agencies have considered, piloted, or imple-
mented PRM for this purpose. We discuss and analyze
the application of PRM to child protection programs,
elaborating on the various misgivings that arise from
the application of predictive modeling to human behav-
ior, and we present a framework to guide the applica-
tion of PRM in child welfare systems. Our framework
considers three core questions: (1) Is PRM more accu-
rate than current practice? (2) Is PRM ethically equiva-
lent or superior to current practice? and (3) Are
necessary evaluative and implementation procedures
established prior to, during, and following introduction
of the PRM?
Keywords: risk assessment; child protective services;
predictive risk modeling; child welfare
The past four decades have witnessed the
dawn of the information age, and with its
arrival has come an array of data tools useful to
researchers. Advances in computer storage have
allowed a vast amount of data to be held essen-
tially for free and processed with breathtaking
speed. Advances in a broad array of analytic
approaches (e.g., propensity score matching,
various regression techniques, machine learning
Brett Drake is a professor at the Brown School at
Washington University in St. Louis. His research inter-
ests include applying big data to understanding child
maltreatment, particularly “front-end” services and
issues of class and race. He has coauthored an ethical
review of potential predictive risk modeling uses in
Melissa Jonson-Reid is the Ralph and Muriel Pumphrey
Professor of Social Work Research and director of the
PhD program in social work at the Brown School at
Washington University in St. Louis. Her research
emphasizes improving outcomes for children in public
child welfare using a systems perspective.
Correspondence: brettd@wustl.edu
applications) have allowed data to be used to more accurately answer a range of
critical practice and policy questions.
Complex algorithms simply could not be executed on large datasets in a reason-
able timeframe on generally available computer platforms 30 years ago. Some
authors of this article recall spending tens of thousands of dollars to procure stor-
age for datasets that took months of processing time to analyze. Far larger datasets
are now transferred routinely across the internet in seconds or minutes, and far
more complex analyses are routinely conducted in minutes or, at worst, hours. Put
simply, sophisticated analytic methods that use big datasets, like predictive risk
modeling (PRM), are newly available for application in fields like child welfare. All
new technologies come with risk, however. It is important to fully understand both
the potential benefits and the potential risks that adoption of PRM might bring.
The obvious question arises: Should we embrace such technologies?
Our Approach to Evaluating Predictive Risk
Models in Child Welfare
There is a natural and quite healthy human tendency to be wary of the new.
Premature adoption of new technologies or practices can have catastrophic
results. One clear example can be found in the heroic actions of Dr. Frances
Oldham Kelsey in forestalling the adoption of Thalidomide in the United States.
Her caution, based on what she found to be inadequate prior trials, along with
other concerns, spared our country the devastating level of impact experienced
by various European countries (Rice 2019). Of course, there have also been his-
torical instances where effective new treatments or interventions have been
unnecessarily delayed. There is a balance that must be found.
This article is concerned with the application of one particular new analytic
tool—PRM—to child welfare programming and policy. PRM is a kind of analysis
that uses existing data and machine learning to predict the likelihood of outcomes
among people. The prediction of human behavior is both innately complex and
has ethical implications. In our view, there are three core conceptual questions
that must be answered to evaluate the use case for PRM: (1) Compared to cur-
rently available tools and current practice, can PRM approaches more accurately
María Gandarilla Ocampo is a social work doctoral student at Washington University in St.
Louis. Her research interests include child maltreatment, child protection systems, and the
impact of mandated reporting policies on families and child welfare system outcomes.
Maria Morrison is a social work doctoral student at Washington University in St Louis. She
has worked for over a decade for the Equal Justice Initiative. Her research focuses on cumula-
tive traumatic stress among incarcerated and formerly incarcerated men in the context of cur-
rent and historical racial injustice.
Darejan (Daji) Dvalishvili is completing her PhD in social work from the Brown School at
Washington University in St. Louis. She has been working with UNICEF and other international
and local nonprofit organizations focusing on child welfare. Her research interests are child
maltreatment, gender-based violence, poverty, and economic strengthening interventions.

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