Modeling Assumptions Clash with the Real World: Transparency, Equity, and Community Challenges for Student Assignment Algorithms

• URL: http://arxiv.org/abs/2101.10367v1
• Date: Mon, 25 Jan 2021 19:29:39 GMT
• Title: Modeling Assumptions Clash with the Real World: Transparency, Equity, and Community Challenges for Student Assignment Algorithms
• Authors: Samantha Robertson, Tonya Nguyen, Niloufar Salehi
• Abstract summary: A growing number of school districts are turning to matching algorithms to assign students to public schools. 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. We argue that direct, ongoing engagement with stakeholders is central to aligning algorithmic values with real world conditions.
• Score: 16.823029377470366
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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