Child Welfare System: Interaction of Policy, Practice and Algorithms

dc.contributor.authorSaxena, Devansh
dc.contributor.authorBadillo-Urquiola, Karla
dc.contributor.authorWisniewski, Pamela
dc.contributor.authorGuha, Shion
dc.date.accessioned2023-03-17T22:48:53Z
dc.date.available2023-03-17T22:48:53Z
dc.date.issued2020
dc.description.abstractThis paper focuses on understanding the collaborative work of multi-disciplinary teams in the child welfare system (CWS). CWS workers participate in meetings mediated by policies in place, current child-welfare practice, as well as algorithms that offer recommendations. We conducted 25 observations of these meetings to assess how algorithms aid decision-making in a domain where decisions often come down to the policies and practices in place. Our findings suggest that the algorithm works fairly well at recommending placement settings, however, these recommendations are often overridden because of policy or legal requirements. Moreover, re-appropriation of the placement algorithm to prescribe the rates for foster parents has led to unintended consequences. This poster identifies uses cases of the algorithm in place, scenarios where conflicts arise between the algorithm and policy/practice, as well as how these conflicts are addressed. Our work identifies a need for human-centered algorithms that can better support child welfare practice.en
dc.identifier.doi10.1145/3323994.3369888
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/4562
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.relation.ispartofCompanion Proceedings of the 2020 ACM International Conference on Supporting Group Work
dc.subjecthuman-centered algorithm design
dc.subjectalgorithmic decision-making
dc.subjectchild welfare system
dc.titleChild Welfare System: Interaction of Policy, Practice and Algorithmsen
dc.typeText/Conference Paper
gi.citation.startPage119–122
gi.citations.count10
gi.citations.elementWenqi Zheng, Emma Walquist, Isha Datey, Xiangyu Zhou, Kelly Berishaj, Melissa Mcdonald, Michele Parkhill, Dongxiao Zhu, Douglas Zytko (2024): “It’s Not What We Were Trying to Get At, but I Think Maybe It Should Be”: Learning How to Do Trauma-Informed Design with a Data Donation Platform for Online Dating Sexual Violence, In: Proceedings of the CHI Conference on Human Factors in Computing Systems, doi:10.1145/3613904.3642045
gi.citations.elementOghenemaro Anuyah, Karla Badillo-Urquiola, Ronald Metoyer (2025): Exploring Knowledge Sharing and Community of Practice Development: A Stakeholders Analysis of Social Service Organizations in a Midwestern Underserved Community, In: The 2025 ACM International Conference on Supporting Group Work, doi:10.1145/3688828.3699645
gi.citations.elementDevansh Saxena, Shion Guha (2020): Conducting Participatory Design to Improve Algorithms in Public Services, In: Companion Publication of the 2020 Conference on Computer Supported Cooperative Work and Social Computing, doi:10.1145/3406865.3418331
gi.citations.elementDevansh Saxena, Seh Young Moon, Dahlia Shehata, Shion Guha (2022): Unpacking Invisible Work Practices, Constraints, and Latent Power Relationships in Child Welfare through Casenote Analysis, In: CHI Conference on Human Factors in Computing Systems, doi:10.1145/3491102.3517742
gi.citations.elementLogan Stapleton, Devansh Saxena, Anna Kawakami, Tonya Nguyen, Asbjørn Ammitzbøll Flügge, Motahhare Eslami, Naja Holten Møller, Min Kyung Lee, Shion Guha, Kenneth Holstein, Haiyi Zhu (2022): Who Has an Interest in “Public Interest Technology”?: Critical Questions for Working with Local Governments & Impacted Communities, In: Companion Publication of the 2022 Conference on Computer Supported Cooperative Work and Social Computing, doi:10.1145/3500868.3560484
gi.citations.elementDevansh Saxena, Charles Repaci, Melanie D Sage, Shion Guha (2022): How to Train a (Bad) Algorithmic Caseworker: A Quantitative Deconstruction of Risk Assessments in Child Welfare, In: CHI Conference on Human Factors in Computing Systems Extended Abstracts, doi:10.1145/3491101.3519771
gi.citations.elementMD Romael Haque, Devansh Saxena, Katy Weathington, Joseph Chudzik, Shion Guha (2024): Are We Asking the Right Questions?: Designing for Community Stakeholders’ Interactions with AI in Policing, In: Proceedings of the CHI Conference on Human Factors in Computing Systems, doi:10.1145/3613904.3642738
gi.citations.elementDevansh Saxena, Erina Seh-Young Moon, Aryan Chaurasia, Yixin Guan, Shion Guha (2023): Rethinking "Risk" in Algorithmic Systems Through A Computational Narrative Analysis of Casenotes in Child-Welfare, In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, doi:10.1145/3544548.3581308
gi.citations.elementDevansh Saxena, Karla Badillo-Urquiola, Pamela J. Wisniewski, Shion Guha (2021): A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare, In: Proceedings of the ACM on Human-Computer Interaction CSCW2(5), doi:10.1145/3476089
gi.citations.elementDevansh Saxena, Shion Guha (2024): Algorithmic Harms in Child Welfare: Uncertainties in Practice, Organization, and Street-level Decision-making, In: ACM Journal on Responsible Computing 1(1), doi:10.1145/3616473
gi.conference.locationSanibel Island, Florida, USA

Files

Collections