Ethical considerations in research when building predictive risk modelling in child and family welfare
DOI:
https://doi.org/10.31265/jcsw.v19i1.619Keywords:
ethics, child and family welfare, Child Protection, predictive risk modelling, machine learning, decision-making, notificationsAbstract
This article presents and discusses ethical issues and implications in research when building a predictive risk model for potential use in Danish child and family welfare. The idea is to build a predictive risk model in order to study whether such a model can be valuable to child and family welfare services in the assessment of risk – aimed specifically at the decision-making process regarding notifications.
Based on a framework developed especially for this field, we present and discuss ethical considerations, reflections and actions in relation to four main ethical principles: non-maleficence, autonomy, justice and explicability. We hope that our reflections on these ethical challenges can inspire research – and potentially also the field of practice when taking a deep dive into the difficult field of digitalization in social work.
References
Ada Lovelace Institute. (2022). Looking before we leap: Ethical review processes for AI and data science research. https://www.adalovelaceinstitute.org/report/lookingbefore-we-leap/Ethics and accountability in practice
Cheng, H. F., Stapleton, L., Kawakami, A., Sivaraman, V., Cheng, Y., Qing, D., ... & Zhu, H. (2022, April). How child welfare workers reduce racial disparities in algorithmic decisions [Conference presentation]. 2022 CHI Conference on Human Factors in Computing Systems, New Orleans. https://doi.org/10.1145/3491102.3501831
Chouldechova, A., Benavides-Prado, D., Fialko, O. & Vaithianathan, R. (2018). A case study of algo-rithm-assisted decision making in child maltreatment hotline screening decisions [Conference presentation]. 1st Conference on Fairness, Accountability and Transparency https://proceedings.mlr.press/v81/chouldechova18a.html.
Coulthard, B., Mallett, J., Taylor, B.J., 2020. Better decisions for children with ‘big data’: can algo-rithms promote fairness, transparency and parental engagement? Societies, 10(4), 97. https://doi:10.3390/soc10040097.
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
Devlieghere J., PGillingham, P. & Roose, R. (2022): Dataism versus relationshipism: a social work perspective, Nordic Social Work Research, 12(3), 328-338. https://doi.org/10.1080/2156857X.2022.2052942
Gilbert, N. (ed). 2007. Combatting Child Abuse: International Perspectives and Trends. Oxford uni-versity press.
Gilbert, P., Parton, N. & Skivenes, M. (eds.). (2011). Child Protection Systems: International Trends and Orientations. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199793358.001.0001
Gillingham, P (2016): Predictive Risk Modelling to Prevent Child Maltreatment and Other Adverse Outcomes for Service Users: Inside the ‘Black Box’ of Machine Learning. British Journal of So-cial Work (2016) 46(4), 1044-1058. https://doi.org/10.1093/bjsw/bcv031
Goldhaber-Fiebert, J. D. & Prince, L. (2019). Impact evaluation of a predictive risk modeling tool for Allegheny county’s child welfare office. Allegheny County.
High-Level Expert Group. (2019). Ethics Guidelines for trustworthy AI. European Commission. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai
High-Level Expert Group on Artificial Intelligence. (2018). A definition of AI: main capabilities and scientific disciplines. European Commission. https://digital-strategy.ec.europa.eu/en/library/definition-artificial-intelligence-main-capabilities-and-scientific-disciplines
Kawakami, A., Sivaraman, V., Cheng, H. F., Stapleton, L., Cheng, Y., Qing, D., ... & Holstein, K. (2022, April). Improving human-AI partnerships in child welfare: understanding worker practic-es, challenges, and desires for algorithmic decision support. [Conference presentation]. 2022 CHI Conference on Human Factors in Computing Systems, New Orleans. https://doi.org/10.1145/3491102.3517439
Keddell, E. (2023). The Devil in the Detail: Algorithmic Risk Prediction Tools and Their Implications for Ethics, Justice and Decision-making. In B. Taylor, J. D. Fluke, J. C. Graham, E. Keddell, C. Kil-lick, A. Shlonsky & A. Whittaker (eds.) The Sage Handbook of Decision Making, Assesment and Risk in Social Work (pp. 405-420). Sage Publications. https://doi.org/10.4135/9781529614657.n51
Kriz, K. & Skivenes, M. (2013). Systemic Differences in Views on Risk: A Comparative Case Vignette Study of Risk Assessment in England, Norway and the United States (California). Child and Youth Services Review, 35(11), 1862–1870. https://doi.org/10.1016/j.childyouth.2013.09.001
Leslie, D., Holmes, L., Hitrova, C. & Ott, E. (2020). Ethics review of machine learning in Children’s social care. The Alan Touring Institute. Oxford University. https://whatworks-csc.org.uk/research-report/ethics-review-of-machine-learning-in-childrens-social-care/
Lehtiniemi, T. (2024). Contextual social valences for artificial intelligence: anticipation that mat-ters in social work. Information, Communication & Society, 27(6), 1110-1125. https://doi.org/10.1080/1369118X.2023.2234987
Lundberg, S.M. & Lee. S. I. (2017) A unified approach to interpreting model predictions. In I. Guy-on, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan & R. Garnett (eds.), Ad-vances in Neural Information Processing Systems 30 (NIPS 2017). https://papers.nips.cc/paper_files/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S. & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1-21. https://doi.org/10.1177/2053951716679679
Morley, J., Floridi, L., Kinsey, L. & Elhalal, A. (2020). From what to how: an initial review of publicly available AI ethics tools, methods and research to translate principles into practices. Science and engineering ethics, 26(4), 2141-2168. https://doi.org/10.1007/s11948-019-00165-5
Munn, L. (2023). The uselessness of AI ethics. AI and Ethics, 3(3), 869-877. https://doi.org/10.1007/s43681-022-00209-w
Pösö, S., Skivenes, M. & Hestbæk, A.-D. (2013). Child Protection Systems within the Danish, Finnish and Norwegian Welfare States – Time for a Child Centric Approach?. European Journal of Social Work 17(4), 475–490. https://doi.org/10.1080/13691457.2013.829802
Rosholm, M., Bodilsen, S. T., Michel, B. & Nielsen, S. A. (2024). Predictive risk modeling for child maltreatment detection and enhanced decision-making: Evidence from Danish administrative data. PLOS ONE, 19(7), e0305974. https://doi.org/10.1371/journal.pone.0305974
Søbjerg, L. M., L. Nirmalajaran & A. M. Villumsen. (2020). Perceptions of Risk and Decisions of Referring Children at Risk. Child Care in Practice 26(2), 130-145. https://doi.org/10.1080/13575279.2019.1685460
Søbjerg, L.M., Taylor, B.J., Przeperski, J., Horvat, S., Nouman, H. & Harvey, D. (2020). Using risk-factor statistics in decision making: prospects and challenges. European Journal of Social Work, 24(5), 788-801. https://doi.10.1080/13691457.2020.1772728
Taylor, B. J. (2020). Teaching and learning decision making in child welfare and protection social work. In J. Fluke, M. López López, R. Benbenishty, E. J. Knorth, & D. J. Baumann (Eds.), Decision making and judgement in child welfare and protection: Theory, research and practice (pp. 281–298). Oxford University Press. https://doi.org/10.1093/oso/9780190059538.003.0013
Vaithianathan, R., Maloney, T., Putnam-Hornstein, E. & Jiang, N. (2013). Children in the public benefit system at risk of maltreatment: Identification via predictive modeling. American Journal of Preventive Medicine, 45(3), 354-359. https://doi.org/10.1016/j.amepre.2013.04.022
Villumsen, A. M. (2017). Hvorfor det ikke er så lige til med udsathed hos børn og unge. In. D. Graversen (ed.), Pædagogik: introduktion til pædagogens grundfaglighed (1st ed.). Hans Reitzels Forlag.
Villumsen, A. M., & Søbjerg, L.M. (2020).: Informal Pathways Informal pathways as a response to limitations in formal categorization of referrals in child and family welfare. Nordic Social Work Research, 13(2), 176-187. https://doi.org/10.1080/2156857X.2020.1795705
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Anne Marie Villumsen, Michael Rosholm, Simon Tranberg Bodilsen, Sanne Dalgaard Toft, Line Svolgaard Berg, Liesanth Yde Nirmalarajan
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.