Modeling Assumptions Clash with the Real World: Transparency, Equity, and Community Challenges for Student Assignment Algorithms
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Abstract (arXiv)
Across the United States, a growing number of school districts are turning to matching algorithms to assign students to public schools. The designers of these algorithms aimed to promote values such as transparency, equity, and community in the process. However, school districts have encountered practical challenges in their deployment. In fact, San Francisco Unified School District voted to stop using and completely redesign their student assignment algorithm because it was not promoting educational equity in practice. We analyze this system using a Value Sensitive Design approach and find that one reason values are not met in practice is that the system relies on modeling assumptions about families' priorities, constraints, and goals that clash with the real world. These assumptions overlook the complex barriers to ideal participation that many families face, particularly because of socioeconomic inequalities. We argue that direct, ongoing engagement with stakeholders is central to aligning algorithmic values with real world conditions. In doing so we must broaden how we evaluate algorithms while recognizing the limitations of purely algorithmic solutions in addressing complex socio-political problems.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jan 2021.
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Citing papers per year: 2023: 4; 2024: 5; 2025: 1; 2026: 1.
11 papers.
- 2023Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Servicescited by 29
- 2023Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiencescited by 21
- 2023`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Valuescited by 18
- 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Toolscited by 7
- 2024Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Usecited by 6
- 2024Statistical Models of Top-k Partial Orderscited by 3
- 2024Legacy Procurement Practices Shape How U.S. Cities Govern AI: Understanding Government Employees' Practices, Challenges, and Needscited by 3
- 2025Talking About the Assumption in the Roomcited by 2
- 2024"It depends": Configuring AI to Improve Clinical Usefulness Across Contextscited by 1
- 2024Beyond Predictive Algorithms in Child Welfarecited by 0
- 2026Large Language Models in Peer-Run Community Behavioral Health Services: Understanding Peer Specialists and Service Users' Perspectives on Opportunities, Risks, and Mitigation Strategiescited by 0
- 2026Large Language Models in Peer-Run Community Behavioral Health Services: Understanding Peer Specialists and Service Users' Perspectives on Opportunities, Risks, and Mitigation Strategies9 Feb 2026
- 2025Talking About the Assumption in the Room18 Feb 2025
- 2024Legacy Procurement Practices Shape How U.S. Cities Govern AI: Understanding Government Employees' Practices, Challenges, and Needs7 Nov 2024
- 2024Statistical Models of Top-k Partial Orders22 Jun 2024
- 2024"It depends": Configuring AI to Improve Clinical Usefulness Across Contexts27 May 2024
- 2024Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Use21 May 2024
- 2024Beyond Predictive Algorithms in Child Welfare26 Feb 2024
- 2023Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences6 Oct 2023
- 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools29 Sep 2023
- 2023`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Values14 Jul 2023
- 2023Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Services17 Mar 2023