Modeling Assumptions Clash with the Real World: Transparency, Equity, and Community Challenges for Student Assignment Algorithms
Samantha Robertson, Tonya Nguyen, Niloufar Salehi
摘要
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 frustrating for families and 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.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper23
- A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-WelfareDevansh Saxena, Karla A. Badillo-Urquiola, Pamela J. Wisniewski, Shion GuhaCSCW 2021 · 被引用 133 次
- Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless ServicesTzu-Sheng Kuo, Hong Shen, Jisoo Geum, Nev Jones 等CHI 2023 · 被引用 82 次
- The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder, Early-stage Deliberations Around Public Sector AI ProposalsAnna Kawakami, Amanda Coston, Haiyi Zhu, Hoda Heidari 等CHI 2024 · 被引用 54 次
- "It is currently hodgepodge": Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI ValuesRama Adithya Varanasi, Nitesh GoyalCHI 2023 · 被引用 52 次
- Rethinking "Risk" in Algorithmic Systems Through A Computational Narrative Analysis of Casenotes in Child-WelfareDevansh Saxena, Erina Seh-Young Moon, Aryan Chaurasia, Yixin Guan 等CHI 2023 · 被引用 35 次
它引用的顶会 Paper2
- A Human-Centered Review of Algorithms used within the U.S. Child Welfare SystemDevansh Saxena, Karla A. Badillo-Urquiola, Pamela J. Wisniewski, Shion GuhaCHI 2020 · 被引用 114 次
- Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based SystemsC. Estelle Smith, Bowen Yu, Anjali Srivastava, Aaron Halfaker 等CHI 2020 · 被引用 77 次
相关 Paper
- Expressiveness, Cost, and Collectivism: How the Design of Preference Languages Shapes Participation in Algorithmic Decision-MakingSamantha Robertson, Tonya Nguyen, Cathy Hu, Catherine Albiston 等CHI 2023 · 被引用 5 次
- Definitions of Fairness Differ Across Socioeconomic Groups & Shape Perceptions of Algorithmic DecisionsTonya Nguyen, Sabriya Alam, Cathy Hu, Catherine Albiston 等CSCW 2024 · 被引用 4 次
- BoundarEase: Fostering Constructive Community Engagement to Inform More Equitable Student Assignment PoliciesCassandra Overney, Cassandra Moe, Alvin Chang, Nabeel GillaniCSCW 2025 · 被引用 6 次
- Power Dynamics and Value Conflicts in Designing and Maintaining Socio-Technical Algorithmic ProcessesJoon Sung Park, Karrie Karahalios, Niloufar Salehi, Motahhare EslamiCSCW 2022 · 被引用 16 次
- Beyond Bias Detection: Community Auditors and Normative Reasoning in AI Oversight CSCW006Corey Jackson, Tallal Ahmad, Shelcia David Raj, Natalie WuCSCW 2026 · 被引用 1 次
