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20232026
most citedDiscretionary Trees: Understanding Street-Level Bureaucracy via Machine Learning

1 citations · 1 across the 8 of their papers we have counts for

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cs.CY2026

Scarcity and Predictive Uncertainty: Implications for Societal Resource Allocation

Shafkat Farabi, Patrick J. Fowler, Sanmay Das

An emerging literature examines the critical question of when and how prediction can be useful in allocating scarce societal resources. We examine a novel variant of this question:…

cs.CY2026

Can LLMs Rank? A Tale of Triads and Triage

Gaurab Pokharel, Shafkat Farabi, Patrick J. Fowler +1

From housing allocation for households experiencing homelessness to triage in emergency departments, LLMs are increasingly being considered as judges of consequential decisions tha…

cs.CY2026

Budgeting Discretion: Theory and Evidence on Street-Level Decision-Making

Gaurab Pokharel, Sanmay Das, Patrick J. Fowler

Street-level bureaucrats, such as caseworkers and border guards routinely face the dilemma of whether to follow rigid policy or exercise discretion based on professional judgement.…

cs.CY2025

Street-Level AI: Are Large Language Models Ready for Real-World Judgments?

Gaurab Pokharel, Shafkat Farabi, Patrick J. Fowler +1

A surge of recent work explores the ethical and societal implications of large-scale AI models that make "moral" judgments. Much of this literature focuses either on alignment with…

cs.CY2025

Who Pays the RENT? Implications of Spatial Inequality for Prediction-Based Allocation Policies

Tasfia Mashiat, Patrick J. Fowler, Sanmay Das

AI-powered scarce resource allocation policies rely on predictions to target either specific individuals (e.g., high-risk) or settings (e.g., neighborhoods). Recent research on ind…