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

• DOI: 10.1177/0002716220978200
• Date: 01 November 2020
• Published date: 01 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

By

BRETT DRAKE,

MELISSA JONSON-REID,

MARÍA GANDARILLA

OCAMPO,

MARIA MORRISON,

and

DAREJAN (DAJI)

DVALISHVILI

978200ANN The Annals Of The American AcademyPredictive Risk Modeling In Child Welfare

research-article2020

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

policy

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

California.

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

PREDICTIVE RISK MODELING IN CHILD WELFARE 163

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.