Algorithmic fairness is framed as a wicked problem solvable via critical pragmatism from urban planning, yielding a flexible framework with recommendations tested on mortgage lending, school choice, and feminicide data cases.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Analysis of Canada's Federal AI Register reveals it frames AI as reliable internal tooling by obscuring sociotechnical elements like human discretion, turning transparency into performative compliance.
Sensitivity analyses of NYC heat emergency indices show that reasonable variations in input variables and spatial scale lead to substantially different risk scores affecting downstream government decisions.
citing papers explorer
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A Critical Pragmatism Approach for Algorithmic Fairness: Lessons from Urban Planning Theory
Algorithmic fairness is framed as a wicked problem solvable via critical pragmatism from urban planning, yielding a flexible framework with recommendations tested on mortgage lending, school choice, and feminicide data cases.
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Bureaucratic Silences: What the Canadian AI Register Reveals, Omits, and Obscures
Analysis of Canada's Federal AI Register reveals it frames AI as reliable internal tooling by obscuring sociotechnical elements like human discretion, turning transparency into performative compliance.
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Scrutinizing Index-Based Risk Assessments: A Case Study in NYC Decision-making for Heat Emergency Management
Sensitivity analyses of NYC heat emergency indices show that reasonable variations in input variables and spatial scale lead to substantially different risk scores affecting downstream government decisions.