# Algorithms and Decision-Making in the Public Sector

arXiv: https://arxiv.org/abs/2106.03673 · HTML: https://mlsanity.com/paper/2106.03673/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2021.

## Built on by (most cited first, 20 of 20)

- [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)
- [Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservables](https://mlsanity.com/paper/2207.13834/) (2022)
- [Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report](https://mlsanity.com/paper/2210.15767/) (2022)
- [On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment](https://mlsanity.com/paper/2507.07341/) (2025)
- [Allocation Requires Prediction Only if Inequality Is Low](https://mlsanity.com/paper/2406.13882/) (2024)
- [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)
- [The Impact of Differential Feature Under-reporting on Algorithmic Fairness](https://mlsanity.com/paper/2401.08788/) (2024)
- [Thoughtful Adoption of NLP for Civic Participation: Understanding Differences Among Policymakers](https://mlsanity.com/paper/2410.22937/) (2024)
- [Legacy Procurement Practices Shape How U.S. Cities Govern AI: Understanding Government Employees' Practices, Challenges, and Needs](https://mlsanity.com/paper/2411.04994/) (2024)
- [The Algorithmic State Architecture (ASA): An Integrated Framework for AI-Enabled Government](https://mlsanity.com/paper/2503.08725/) (2025)
- [Governance and Communication of Algorithmic Decision Making: A Case Study on Public Sector](https://mlsanity.com/paper/2110.09226/) (2021)
- [The Hidden Cost of Waiting for Accurate Predictions](https://mlsanity.com/paper/2503.00650/) (2025)
- [One Bad NOFO? AI Governance in Federal Grantmaking](https://mlsanity.com/paper/2505.08133/) (2025)
- [Recentering Validity Considerations through Early-Stage Deliberations Around AI and Policy Design](https://mlsanity.com/paper/2303.14602/) (2023)
- [FairTargetSim: An Interactive Simulator for Understanding and Explaining the Fairness Effects of Target Variable Definition](https://mlsanity.com/paper/2403.06031/) (2024)
- [Stability of AI Governance Systems: A Coupled Dynamics Model of Public Trust and Social Disruptions](https://mlsanity.com/paper/2603.20248/) (2026)
- [Hide&Seek: Learning to Explain in an End-to-End Differentiable Network](https://mlsanity.com/paper/2608.16689/) (2026)

## Abstract (arXiv)

This article surveys the use of algorithmic systems to support decision-making in the public sector. Governments adopt, procure, and use algorithmic systems to support their functions within several contexts -- including criminal justice, education, and benefits provision -- with important consequences for accountability, privacy, social inequity, and public participation in decision-making. We explore the social implications of municipal algorithmic systems across a variety of stages, including problem formulation, technology acquisition, deployment, and evaluation. We highlight several open questions that require further empirical research.