Modeling Assumptions Clash with the Real World: Transparency, Equity, and Community Challenges for Student Assignment Algorithms
arXiv:2101.10367 · doi:10.1145/3411764.3445748
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.
References in corpus (1)
Cited by in corpus (9)
- Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Services
- `It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Values
- The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder Early-stage Deliberations Around Public Sector AI Proposals
- Unpacking Invisible Work Practices, Constraints, and Latent Power Relationships in Child Welfare through Casenote Analysis
- Are We Asking the Right Questions?: Designing for Community Stakeholders' Interactions with AI in Policing
- Practitioners Versus Users: A Value-Sensitive Evaluation of Current Industrial Recommender System Design
- "It depends": Configuring AI to Improve Clinical Usefulness Across Contexts
- A Human-Centered Review of Algorithms in Homelessness Research
- Statistical Models of Top- Partial Orders