A Human-Centered Review of Algorithms used within the U.S. Child Welfare System
Open accessAbstract
The U.S. Child Welfare System (CWS) is charged with improving outcomes for foster youth; yet, they are overburdened and underfunded. To overcome this limitation, several states have turned towards algorithmic decision-making systems to reduce costs and determine better processes for improving CWS outcomes. Using a human-centered algorithmic design approach, we synthesize 50 peer-reviewed publications on computational systems used in CWS to assess how they were being developed, common characteristics of predictors used, as well as the target outcomes. We found that most of the literature has focused on risk assessment models but does not consider theoretical approaches (e.g., child-foster parent matching) nor the perspectives of caseworkers (e.g., case notes). Therefore, future algorithms should strive to be context-aware and theoretically robust by incorporating salient factors identified by past research. We provide the HCI community with research avenues for developing human-centered algorithms that redirect attention towards more equitable outcomes for CWS.
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Public-sector practice often uses aggregate administrative data that is poorly collected.
“Second, current practices of using aggregate administrative data that is often poorly collected [31,53,107] and biased to predict an individual's behavior is a complex and hard task that may lead to unfairness in decision-making outcomes and is, in most western, liberal democratic systems unconstitutional and/or illegal [123].”
From A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare (2021) · cites this paper as [107]
The “Sociotechnical Systems for Child-Welfare” section describes state child-welfare data systems used for decision-making and national databases. The “Inferential Statistics approaches” section says data in early child-welfare algorithm studies was limited and of questionable quality because collection was not standardized. That supports a data-quality concern in this setting, but not the broader claim that aggregate administrative data in public-sector practice is often poorly collected. (LLM, conf=0.84)
Experimental — accuracy not yet measuredPublic-sector outcomes such as recidivism risk or welfare need are poorly and inconsistently defined.
“First, outcomes in the public sector like assessing risk of recidivism in criminal justice or assessing need in welfare services are poorly and inconsistently defined [11,42,55,107,129].”
From A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare (2021) · cites this paper as [107]
In “Improve Lives and not just ‘Minimize Risk’ (RQ3)” (page unavailable), the review says that the child-welfare outcome “risk of maltreatment” is poorly defined and that state definitions vary. This supports the child-welfare risk example, but the passage does not establish the claim for recidivism risk or for definitions of welfare need. (LLM, conf=0.87)
Experimental — accuracy not yet measuredPrior research shows workers let decision-aids supplant their own decisions when they lack confidence or experience.
“However, prior research shows that workers allow decision-aids to supplant their own decisions when they lack confidence and/or experience [42,107,113].”
From A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare (2021) · cites this paper as [107]
The Risk Assessment section says child-welfare caseworkers must rely on decision tools, especially in cases of high uncertainty. The section on theoretical and context-aware algorithms says those tools can force workers to abandon contextual judgments. Neither passage says this happens because workers lack confidence or experience. (LLM, conf=0.84)
Experimental — accuracy not yet measuredChild-welfare risk-assessment algorithms have traditionally used only child and parent characteristics as predictors.
“For instance, risk assessment algorithms in child-welfare have traditionally only used a narrow set of predictors (child and parent characteristics) to assess risk [107].”
From A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare (2021) · cites this paper as [107]
In “Going Beyond what is ‘Easily Quantifiable’ (RQ2)” (page not provided), the review says a majority of algorithms used predictors concerning child and parent characteristics. It also criticizes risk estimates based on a narrow set of predictors, but it does not say these algorithms used only child and parent characteristics; “majority” does not support that absolute claim. (LLM, conf=0.88)
Experimental — accuracy not yet measuredLaw determines which data are available for predictive modeling and how target outcomes are defined.
“For instance, law dictates which data is available for predictive modeling and how target outcomes are defined [107,114].”
From A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare (2021) · cites this paper as [107]
The “Improve Lives and not just ‘Minimize Risk’ (RQ3)” section says federal and state law define child abuse and neglect, and that state laws and policies affect substantiation judgments used to train algorithms. This supports the point about target outcomes. The “Going Beyond what is ‘Easily Quantifiable’ (RQ2)” section discusses which information becomes available for modeling, but does not say law determines that availability; that part of the claim is unsupported. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Prior work argues that public-sector algorithms are a distinct domain and must be characterized differently from private-sector algorithms where outcomes are well-defined.
“Prior work has argued that algorithms in the public sector is a domain in its own right [34,62,107,109,121] and must be characterized differently as compared to algorithms in the private sector where the decision outcomes are well-defined.”
From A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare (2021) · cites this paper as [107]
The sections “Algorithms Need to be theoretical & context-aware (RQ1)” and “Bridging AI and HCI Through Participatory Design” argue that child-welfare algorithms must account for context and that their outcomes require careful definition and measurement (page numbers unavailable). This supports treating algorithms in this public-service setting differently, but the paper does not establish the claim for the public sector generally or compare it with private-sector algorithms whose outcomes are well-defined. (LLM, conf=0.82)
Experimental — accuracy not yet measuredPublic-sector algorithms have drawn criticism for worse outcomes, racial bias, and structural inequality in strained, underfunded systems.
“Consequently, they have also been scrutinized for achieving worse outcomes, exacerbating racial biases, and strengthening structural inequalities [42,101,107] within systems that are overburdened and under-resourced, yet critically needed [33,75].”
From A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare (2021) · cites this paper as [107]
This deficit-based focus diverts attention from strength-based frameworks.
“This is driving attention away from strength-based frameworks [9,20,99,107].”
From A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare (2021) · cites this paper as [107]
A systematic review of U.S. child-welfare algorithms found discrepancies in data, computational methods, and target outcomes.
“Saxena et al. [107] recently conducted a systematic review of algorithms employed in the U.S. child-welfare system and uncovered several discrepancies in regard to data, computational methods, and target outcomes of these algorithms [107].”
From A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare (2021) · cites this paper as [107]
A more comprehensive understanding of risk is needed to account for family social support and risks posed by the system itself.
“However, a more comprehensive understanding about the accumulation of risk is necessary to account for the family's social support system as well as the risk posed by the system itself [107].”
From A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare (2021) · cites this paper as [107]
Differing socio-technical imaginations of public-sector algorithms are consistent with existing literature.
“Differing socio-technical imaginations of algorithms in the public sector are aligned well with existing literature [20,62,103,107,112].”
From A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare (2021) · cites this paper as [107]
Prior literature supports the benefits of strength-based approaches and documents the pitfalls of repurposing algorithmic outcomes for different purposes.
“The benefits of strength-based approaches have been supported in prior literature [9,99,107,119] as well as the pitfalls for reappropriating algorithmic outcomes for different purposes [79,106].”
From A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare (2021) · cites this paper as [107]
The citation to this paper is attached to the claim about reappropriating algorithmic outcomes. The 'Improve Lives and not just “Minimize Risk” (RQ3)' section discusses negative interpretations of risk-model outputs, and 'Risk Assessment' discusses difficulties using decision tools, but neither documents outcomes being repurposed for different purposes. Searches for reappropriation, repurposing, and unintended uses found no passage establishing that point. (LLM, conf=0.70)
Experimental — accuracy not yet measured
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“This is driving attention away from strength-based frameworks [9, 20, 99, 107].”
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Faster Information for Effective Long-Term Discharge: A Field Study in Adult Foster Care
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Zehrung, R., Cawelti, L., Steenis, E. J. V., Soria, V. P., Mendoza, G. A., Arum, R., Chen, Y., & Ahn, J. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757449 · Cited by 2 · see citing works
A computational social science approach to understanding predictors of Chafee service receipt
Yan, J., Hall, S. F., Sage, M., Du, Y., & Joseph, K. (2024) · Children and Youth Services Review · doi:10.1016/j.childyouth.2024.107454 · Cited by 4 · see citing works
Fowler, J., & Zachry, M. R. (2023) · IEEE International Professional Communication Conference (ProComm) · doi:10.1109/procomm57838.2023.00021 · Cited by 2 · see citing works
Caseload factors predictive of family abuse and neglect treatment outcomes
Rhoades, K. A., Nichols, S. R., Slep, A. M. S., & Heyman, R. E. (2024) · Child Abuse & Neglect · doi:10.1016/j.chiabu.2024.106887 · Cited by 2 · see citing works
Investigating the Relative Strengths of Humans and Machine Learning in Decision-Making
Rastogi, C. (2023) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3600211.3604738 · Cited by 2 · see citing works
Saxena, D., & Guha, S. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4077245 · Cited by 2 · see citing works
Self-citationMøller, N. H., & Cohn, M. L. (2023) · Human-computer interaction series · doi:10.1007/978-3-031-31642-5_5 · Cited by 1 · see citing works
The Hidden Workload: Student Data Work in Multimodal Algorithmic Evaluations
Méndez, G. G., Benjumea, J. H., Eras, L., Domínguez, F., & Wong-Villacrés, M. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757672 · Cited by 1 · see citing works
Mitigating Algorithmic Biases through Incentive-Based Rating Systems
Lee, N. T. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197579329.013.33
- Authors
- Saxena, D., Badillo-Urquiola, K., Wisniewski, P. J., & Guha, S.
- Affiliations
- Marquette University (US)
- University of Central Florida (US)
- University of Central Florida, Orlando, FL, USA
- Venue
- Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2020-04-21
- Pages
- 1-15
- OpenAlex
- W3009647070
- Cited by
- 116
- References
- 93
- Citations by year
- 2026: 8 · 2025: 20 · 2024: 28 · 2023: 23 · 2022: 22 · 2021: 12 · 2020: 3
- FWCI
- 15.56
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- e-Publications@Marquette (Marquette University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Facebook Computational Social Science Methodology Research Award
- Northwestern Mutual Data Science Institute
- William T. Grant Foundation (1067525)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Safety Research › Child Welfare and Adoption
- Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
- Keywords
- child welfare system, algorithmic decision-making, human-centered design, risk assessment model, foster youth, case law commentary, algorithmic fairness, predictive modeling, child welfare casework
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