Child Welfare System: Interaction of Policy, Practice and Algorithms
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This 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.
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Number of citations to item: 9
- Wenqi 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
- Devansh 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
- Devansh 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
- Devansh 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
- MD 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
- Devansh 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
- Devansh 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
- Devansh 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
- Logan 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