# A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare

arXiv: https://arxiv.org/abs/2107.03487 · HTML: https://mlsanity.com/paper/2107.03487/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2021.

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

- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support](https://mlsanity.com/paper/2204.02310/) (2022)
- [A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms](https://mlsanity.com/paper/2206.14983/) (2022)
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- [Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir"](https://mlsanity.com/paper/2111.01122/) (2021)
- [Toward General Design Principles for Generative AI Applications](https://mlsanity.com/paper/2301.05578/) (2023)
- [Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders](https://mlsanity.com/paper/2205.08928/) (2022)
- [AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development](https://mlsanity.com/paper/2502.18682/) (2025)
- [Contestable Camera Cars: A Speculative Design Exploration of Public AI That Is Open and Responsive to Dispute](https://mlsanity.com/paper/2302.04603/) (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)
- [(Beyond) Reasonable Doubt: Challenges that Public Defenders Face in Scrutinizing AI in Court](https://mlsanity.com/paper/2403.13004/) (2024)
- [Thoughtful Adoption of NLP for Civic Participation: Understanding Differences Among Policymakers](https://mlsanity.com/paper/2410.22937/) (2024)
- [Analysing and Organising Human Communications for AI Fairness-Related Decisions: Use Cases from the Public Sector](https://mlsanity.com/paper/2404.00022/) (2024)
- [Talking About the Assumption in the Room](https://mlsanity.com/paper/2502.13268/) (2025)
- [Key Considerations for Domain Expert Involvement in LLM Design and Evaluation: An Ethnographic Study](https://mlsanity.com/paper/2602.14357/) (2026)
- ["Is This Not Enough?": Asymmetries in Institutional Accountability and Collective Sensemaking in the Case of Canada's Algorithmic Visa Triage System](https://mlsanity.com/paper/2606.13071/) (2026)
- [Beyond Predictive Algorithms in Child Welfare](https://mlsanity.com/paper/2403.05573/) (2024)
- [Toward AI Matching Policies in Homeless Services: A Qualitative Study with Policymakers](https://mlsanity.com/paper/2508.07129/) (2025)
- [Bureaucratic Silences: What the Canadian AI Register Reveals, Omits, and Obscures](https://mlsanity.com/paper/2604.15514/) (2026)
- [Fairness Audits of Institutional Risk Models in Deployed ML Pipelines](https://mlsanity.com/paper/2604.19468/) (2026)
- [A Global Comparison of Schemas, Transparency, and Interoperability in Public-Sector AI Registers and Inventories](https://mlsanity.com/paper/2609.24883/) (2026)

## Abstract (arXiv)

Algorithms have permeated throughout civil government and society, where they are being used to make high-stakes decisions about human lives. In this paper, we first develop a cohesive framework of algorithmic decision-making adapted for the public sector (ADMAPS) that reflects the complex socio-technical interactions between textithuman discretion, textitbureaucratic processes, and textitalgorithmic decision-making by synthesizing disparate bodies of work in the fields of Human-Computer Interaction (HCI), Science and Technology Studies (STS), and Public Administration (PA). We then applied the ADMAPS framework to conduct a qualitative analysis of an in-depth, eight-month ethnographic case study of the algorithms in daily use within a child-welfare agency that serves approximately 900 families and 1300 children in the mid-western United States. Overall, we found there is a need to focus on strength-based algorithmic outcomes centered in social ecological frameworks. In addition, algorithmic systems need to support existing bureaucratic processes and augment human discretion, rather than replace it. Finally, collective buy-in in algorithmic systems requires trust in the target outcomes at both the practitioner and bureaucratic levels. As a result of our study, we propose guidelines for the design of high-stakes algorithmic decision-making tools in the child-welfare system, and more generally, in the public sector. We empirically validate the theoretically derived ADMAPS framework to demonstrate how it can be useful for systematically making pragmatic decisions about the design of algorithms for the public sector.