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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

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.

Keywords

Family violence Child welfare Infants, decision making 

Notes

References

  1. Baltimore City Child Fatality Review Team, Subcommittee on Child Abuse and Neglect. (2017). Eliminating child abuse and neglect fatalities in Baltimore City. Retrieved from http://healthybabiesbaltimore.com/uploads/files/Initiatives/Baltimore%20City%20CFR%20Child%20Abuse%20Report%20January%202017.pdf
  2. Banerjee, A., Bandyopadhyay, T., & Acharya, P. (2013). Data analytics: Hyped up aspirations or true potential? Vikalpa, 38(4), 1–12.  https://doi.org/10.1177/0256090920130401.CrossRefGoogle Scholar
  3. Barth, R. P., Putnam-Hornstein, E., Shaw, T. V., & Dickinson, N. S. (2016). Safe children: Reducing severe and fatal maltreatment (grand challenges for social work initiative working paper no. 17). Cleveland: American Academy of Social Work and Social Welfare Retrieved from http://grandchallengesforsocialwork.org/wp-content/uploads/2015/12/WP17-with-cover.pdf.Google Scholar
  4. Brauneis, R., & Goodman, E. P. (2018). Algorithmic transparency for the smart city. Yale Journal of Law & Technology, 20, 103–176 Retrieved from https://yjolt.org/sites/default/files/20_yale_j._l._tech._103.pdf.Google Scholar
  5. Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358(6370), 1530–1534.  https://doi.org/10.1126/science.aap8062.CrossRefPubMedGoogle Scholar
  6. Chouldechova, A., Benavides-Prado, D., Fialko, O., & Vaithianathan, R. (2018). A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. Proceedings of Machine Learning Research, 81, 1–18.Google Scholar
  7. Christian, B., & Griffiths, T. (2016). Algorithms to live by: The computer science of human decisions. New York: Henry Holt and Company.Google Scholar
  8. Chu, X., Ilyas, I. F., Krishnan, S., & Wang, J. (2016). Data cleaning: Overview and emerging challenges. In Proceedings of the 2016 international conference on Management of Data (pp. 2201–2206). New York: ACM.  https://doi.org/10.1145/2882903.2912574.CrossRefGoogle Scholar
  9. Commission to Eliminate Child Abuse, & Fatalities, N. (2016). Within our reach: A national strategy to eliminate child abuse and neglect fatalities. Washington, DC: Government Printing Office.Google Scholar
  10. Cook, T. D., Tang, Y., & Seidman Diamond, S. (2014). Causally valid relationships that invoke the wrong causal agent: Construct validity of the cause in policy research. Journal of the Society for Social Work and Research, 5(4), 379–414.  https://doi.org/10.1086/679289.CrossRefGoogle Scholar
  11. County of Los Angeles Office of Child Protection. (2017). Examination of using structured decision making and predictive analytics in assessing safety and risk in child welfare (item no. 490A, agenda of September 20th, 2016). Retrieved from http://file.lacounty.gov/SDSInter/bos/bc/1023048_05.04.17OCPReportonRiskAssessmentTools_SDMandPredictiveAnalytics_.pdf
  12. Cuccaro-Alamin, S., Foust, R., Vaithianathan, R., & Putnam-Hornstein, E. (2017). Risk assessment and decision making in child protective services: Predictive risk modeling in context. Children and Youth Services Review, 79, 291–298.  https://doi.org/10.1016/j.childyouth.2017.06.027.CrossRefGoogle Scholar
  13. D'andrade, A., Austin, M. J., & Benton, A. (2008). Risk and safety assessment in child welfare: Instrument comparisons. Journal of Evidence-Based Social Work, 5(1–2), 31–56.  https://doi.org/10.1300/J394v05n01_03.CrossRefPubMedGoogle Scholar
  14. de Haan, I., & Connolly, M. (2014). Another Pandora's box? Some pros and cons of predictive risk modeling. Children and Youth Services Review, 47, 86–91.  https://doi.org/10.1016/j.childyouth.2014.07.016.CrossRefGoogle Scholar
  15. Doyle, J. J., Jr. (2013). Causal effects of foster care: An instrumental-variables approach. Children and Youth Services Review, 35, 1143–1151.  https://doi.org/10.1016/j.childyouth.2011.03.014.CrossRefGoogle Scholar
  16. DuBois, J. M. (2008). Ethics in mental health research: Principles, guidance, cases. New York: Oxford University Press.Google Scholar
  17. Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. New York: St. Martin's Press.Google Scholar
  18. Foster, E. M., & McCombs-Thornton, K. (2013). Child welfare and the challenge of causal inference. Children and Youth Services Review, 35, 1130–1142.  https://doi.org/10.1016/j.childyouth.2011.03.012.CrossRefGoogle Scholar
  19. Garcia, M. (2016). Racist in the machine: The disturbing implications of algorithmic bias. World Policy Journal, 33(4), 111–117.  https://doi.org/10.1215/07402775-3813015.CrossRefGoogle Scholar
  20. Gershenfeld, N., Krikorian, R., & Cohen, D. (2004, October). The internet of things. Scientific American, 291(4), 76–81.  https://doi.org/10.1038/scientificamerican1004-76.CrossRefPubMedGoogle Scholar
  21. Gilbert, R., Widom, C. S., Browne, K., Fergusson, D., Webb, E., & Janson, S. (2009). Burden and consequences of child maltreatment in high-income countries. Lancet, 373(9657), 68–81.  https://doi.org/10.1016/S0140-6736(08)61706-7.CrossRefPubMedGoogle Scholar
  22. Google. (2018). How search works. Retrieved from https://www.google.com/search/howsearchworks/ Google Scholar
  23. Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182.Google Scholar
  24. Harcourt, B. E. (2007). Against prediction: Profiling, policing, and punishing in an actuarial age. Chicago: University of Chicago Press.Google Scholar
  25. Humphreys, K. L., Gleason, M. M., Drury, S. S., Miron, D., Nelson, C. A., 3rd, Fox, N. A., & Zeanah, C. H. (2015). Effects of institutional rearing and foster care on psychopathology at age 12 years in Romania: Follow-up of an open, randomised controlled trial. Lancet Psychiatry, 2(7), 625–634.  https://doi.org/10.1016/S2215-0366(15)00095-4.CrossRefPubMedPubMedCentralGoogle Scholar
  26. Jonson-Reid, M., Kohl, P. L., & Drake, B. (2012). Child and adult outcomes of chronic child maltreatment. Pediatrics, 129(5), 839–845.  https://doi.org/10.1542/peds.2011-2529.CrossRefPubMedPubMedCentralGoogle Scholar
  27. Kenton, W. (2017). Black swan. Retrieved from https://www.investopedia.com/terms/b/blackswan.asp
  28. Kim, H., Drake, B., & Jonson-Reid, M. (2018). An examination of class-based visibility bias in national child maltreatment reporting. Children and Youth Services Review, 85, 165–173.  https://doi.org/10.1016/j.childyouth.2017.12.019.CrossRefGoogle Scholar
  29. Kulkarni, S. J., Barth, R. P., & Messing, J. T. (2016). Policy recommendations for meeting the Grand Challenge to Stop Family Violence (grand challenges for social work initiative policy brief no. 3). Cleveland: American Academy of Social Work & Social Welfare Retrieved from https://openscholarship.wustl.edu/cgi/viewcontent.cgi?article=1794&context=csd_research.Google Scholar
  30. Lawrence, C. R., Carlson, E. A., & Egeland, B. (2006). The impact of foster care on development. Development and Psychopathology, 18(1), 57–76.  https://doi.org/10.1017/S0954579406060044.CrossRefPubMedGoogle Scholar
  31. Marr, B. (2018, May). How much data do we create everyday? The mind-blowing stats everyone should read. Forbes. Retrieved from https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#2667f3d460ba.
  32. Meissner, C. A., & Brigham, J. C. (2001). Thirty years of investigating the own-race bias in memory for faces: A meta-analytic review. Psychology, Public Policy, and Law, 7(1), 3–35.  https://doi.org/10.1037/1076-8971.7.1.3.CrossRefGoogle Scholar
  33. Mendoza, N. S., Rose, R. A., Geiger, J. M., & Cash, S. J. (2016). Risk assessment with actuarial and clinical methods: Measurement and evidence-based practice. Child Abuse & Neglect, 61, 1–12.  https://doi.org/10.1016/j.chiabu.2016.09.004.CrossRefGoogle Scholar
  34. Meyer, A. S., & Moore, A. A. (2015). The future of termination of parental rights research and practice: A commentary on ben-David. Journal of Family Social Work, 18(4), 253–266.  https://doi.org/10.1080/10522158.2015.1079585.CrossRefGoogle Scholar
  35. Newell, S., & Marabelli, M. (2015). Strategic opportunities (and challenges) of algorithmic decision-making: A call for action on the long-term societal effects of “datification”. Journal of Strategic Information Systems, 24(1), 3–14.  https://doi.org/10.1016/j.jsis.2015.02.001.CrossRefGoogle Scholar
  36. Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York: NYU Press.CrossRefGoogle Scholar
  37. Pearl, J., & Mackenzie, D. (2018). The book of why: The new science of cause and effect. New York: Basic Books.Google Scholar
  38. Protect Our Kids Act of 2012. U.S.C. 42 §1305 [Page 126 STAT. 2460; Pub. L.112–275]. (2013).Google Scholar
  39. Prudente, T. & Calvert, S. (2018). For the second time, Baltimore man is charged in the death of a child. The Baltimore Sun, retrieved from https://www.baltimoresun.com/news/maryland/crime/bs-md-ci-baby-murder-charges-20180723-story.html
  40. Putnam-Hornstein, E. (2011). Report of maltreatment as a risk factor for injury death: A prospective birth cohort study. Child Maltreatment, 16(3), 163–174.  https://doi.org/10.1177/1077559511411179.CrossRefPubMedGoogle Scholar
  41. Rose, R. A. (2018). Frameworks for credible causal inference in observational studies of family violence. Journal of Family Violence, advance online publication.  https://doi.org/10.1007/s10896-018-0011-3.CrossRefGoogle Scholar
  42. Rose, R. A., & Stone, S. I. (2011). Instrumental variable estimation in social work research: A technique for estimating causal effects in nonrandomized settings. Journal of the Society for Social Work and Research, 2, 76–88.  https://doi.org/10.5243/jsswr.2011.4.CrossRefGoogle Scholar
  43. Rubin, D. M., O'Reilly, A. L., Luan, X., & Localio, A. R. (2007). The impact of placement stability on behavioral well-being for children in foster care. Pediatrics, 119(2), 336–344.  https://doi.org/10.1542/peds.2006-1995.CrossRefPubMedPubMedCentralGoogle Scholar
  44. Russell, J. (2015). Predictive analytics and child protection: Constraints and opportunities. Child Abuse & Neglect, 46, 182–189.  https://doi.org/10.1016/j.chiabu.2015.05.022.CrossRefGoogle Scholar
  45. Sankaran, V. S. (2017). Child welfare's scarlet letter: How a prior termination of parental rights can permanently brand a parent as unfit. N.Y.U. Review of Law & Social Change, 41(4), 685–705 Retrieved from https://socialchangenyu.com/wp-content/uploads/2017/11/sankaran.pdf.Google Scholar
  46. Schwartz, I. M., York, P., Nowakowski-Sims, E., & Ramos-Hernandez, A. (2017). Predictive and prescriptive analytics, machine learning and child welfare risk assessment: The Broward County experience. Children and Youth Services Review, 81, 309–320.  https://doi.org/10.1016/j.childyouth.2017.08.020.CrossRefGoogle Scholar
  47. Shaw, T. V., Barth, R. P., Mattingly, J., Ayer, D., & Berry, S. (2013). Child welfare birth match: Timely use of child welfare administrative data to protect newborns. Journal of Public Child Welfare, 7(2), 217–234.  https://doi.org/10.1080/15548732.2013.766822.CrossRefGoogle Scholar
  48. Sobočan, A. M., Bertotti, T., & Strom-Gottfried, K. (2018). Ethical considerations in social work research. European Journal of Social Work, advance online publication.  https://doi.org/10.1080/13691457.2018.1544117.CrossRefGoogle Scholar
  49. Taleb, N. (2007). The black swan: The impact of the highly improbable. New York: Random House.Google Scholar
  50. U.S. Department of Health & Human Services, Administration for Children and Families, Administration on Children, Youth and Families, Children’s Bureau. (2018). Child maltreatment 2016. Available from https://www.acf.hhs.gov/cb/research-data-technology/statistics-research/child-maltreatment
  51. Vaithianathan, R., Rouland, B., & Putnam-Hornstein, E. (2018). Injury and mortality among children identified as at high risk of maltreatment. Pediatrics, 141(2), e20172882.  https://doi.org/10.1542/peds.2017-2882.CrossRefPubMedGoogle Scholar
  52. Wickham, H., & Grolemund, G. (2016). R for data science: Import, tidy, transform, visualize, and model data. Sebastopol: O'Reilly Media.Google Scholar

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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