Preventing Infant Maltreatment with Predictive Analytics: Applying Ethical Principles to Evidence-Based Child Welfare Policy

Paul Lanier, Maria Rodriguez, Sarah Verbiest, Katherine Bryant, Ting Guan, Adam Zolotor

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Infant maltreatment is a devastating social and public health problem. Birth Match is an innovative policy solution to prevent infant maltreatment that leverages existing data systems to rapidly predict future risk through linkage of birth certificate and child welfare data then initiate a child protection response. Birth Match is one example of child welfare policy that capitalizes on recent advances in computing technology, predictive analytics, and algorithmic decision making. We apply frameworks from business and computer science as a case study in ethical decision-making in child welfare policy. Current Birth Match policy applications appear to lack key aspects of transparency and accountability identified in the frameworks. Although technology holds promise to help solve intractable social problems such as fatal infant maltreatment, the decision to deploy such policy innovations must consider ethical questions and tradeoffs. Technological advances hold great promise for prevention of fatal infant maltreatment, but numerous ethical considerations are lacking in current implementation and should be considered in future applications.

Original languageEnglish (US)
Pages (from-to)1-13
Number of pages13
JournalJournal of Family Violence
Volume35
Issue number1
DOIs
StatePublished - Jan 1 2020
Externally publishedYes

Keywords

  • Child welfare
  • Family violence
  • Infants, decision making

ASJC Scopus subject areas

  • Clinical Psychology
  • Sociology and Political Science
  • Social Sciences (miscellaneous)
  • Law

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