Journal of Family Violence

, Volume 35, Issue 1, pp 1–13 | Cite as

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

  • Paul LanierEmail author
  • Maria Rodriguez
  • Sarah Verbiest
  • Katherine Bryant
  • Ting Guan
  • Adam Zolotor
Original Article


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.


Family violence Child welfare Infants, decision making 



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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Social WorkUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.Jordan Institute for FamiliesUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.Injury Prevention Research CenterUniversity of North Carolina at Chapel HillChapel HillUSA
  4. 4.Silberman School of Social WorkHunter CollegeNew York CityUSA
  5. 5.Center for Maternal and Infant HealthUniversity of North Carolina at Chapel HillChapel HillUSA
  6. 6.School of Medicine, Department of Family MedicineUniversity of North Carolina at Chapel HillChapel HillUSA

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