# A Human-Centered Review of the Algorithms used within the U.S. Child Welfare System

arXiv: https://arxiv.org/abs/2003.03541 · HTML: https://mlsanity.com/paper/2003.03541/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2020.

## Built on by (most cited first, 23 of 23)

- [Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support](https://mlsanity.com/paper/2204.02310/) (2022)
- [Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Services](https://mlsanity.com/paper/2303.09743/) (2023)
- [A Taxonomy of Human and ML Strengths in Decision-Making to Investigate Human-ML Complementarity](https://mlsanity.com/paper/2204.10806/) (2022)
- [Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservables](https://mlsanity.com/paper/2207.13834/) (2022)
- [Leveraging Expert Consistency to Improve Algorithmic Decision Support](https://mlsanity.com/paper/2101.09648/) (2021)
- [Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders](https://mlsanity.com/paper/2205.08928/) (2022)
- [The Impact of Algorithmic Risk Assessments on Human Predictions and its Analysis via Crowdsourcing Studies](https://mlsanity.com/paper/2109.01443/) (2021)
- [AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development](https://mlsanity.com/paper/2502.18682/) (2025)
- [Mention Annotations Alone Enable Efficient Domain Adaptation for Coreference Resolution](https://mlsanity.com/paper/2210.07602/) (2022)
- [A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI](https://mlsanity.com/paper/2302.05284/) (2023)
- [Examining risks of racial biases in NLP tools for child protective services](https://mlsanity.com/paper/2305.19409/) (2023)
- [Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledge](https://mlsanity.com/paper/2308.15700/) (2023)
- ["This is not a data problem": Algorithms and Power in Public Higher Education in Canada](https://mlsanity.com/paper/2403.13969/) (2024)
- [Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Use](https://mlsanity.com/paper/2405.12458/) (2024)
- [A Human-Centered Review of Algorithms in Decision-Making in Higher Education](https://mlsanity.com/paper/2302.05839/) (2023)
- [From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions](https://mlsanity.com/paper/2406.18865/) (2024)
- [Secondary Stakeholders in AI: Fighting for, Brokering, and Navigating Agency](https://mlsanity.com/paper/2506.07281/) (2025)
- [Lost in Translation: Reimagining the Machine Learning Life Cycle in Education](https://mlsanity.com/paper/2209.03929/) (2022)
- ["It is there, and you need it, so why do you not use it?" Achieving better adoption of AI systems by domain experts, in the case study of natural science research](https://mlsanity.com/paper/2403.16895/) (2024)
- [A Risk Taxonomy and Reflection Tool for Large Language Model Adoption in Public Health](https://mlsanity.com/paper/2411.02594/) (2024)
- [A Conceptual Framework for Using Machine Learning to Support Child Welfare Decisions](https://mlsanity.com/paper/2207.05855/) (2022)
- [Beyond Predictive Algorithms in Child Welfare](https://mlsanity.com/paper/2403.05573/) (2024)
- [Bureaucratic Silences: What the Canadian AI Register Reveals, Omits, and Obscures](https://mlsanity.com/paper/2604.15514/) (2026)

## Abstract (arXiv)

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.