# Discretionary Trees: Understanding Street-Level Bureaucracy via Machine Learning

arXiv: https://arxiv.org/abs/2312.10694 · HTML: https://mlsanity.com/paper/2312.10694/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 17 Dec 2023.

## Builds on (most cited first, 6 of 6)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Learning Optimized Risk Scores](https://mlsanity.com/paper/1610.00168/) (2016)
- [Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support](https://mlsanity.com/paper/2204.02310/) (2022)
- [Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Services](https://mlsanity.com/paper/2303.09743/) (2023)
- [Robust Active Preference Elicitation](https://mlsanity.com/paper/2003.01899/) (2020)

## Built on by (most cited first, 5 of 5)

- [Allocation Requires Prediction Only if Inequality Is Low](https://mlsanity.com/paper/2406.13882/) (2024)
- [Beyond Eviction Prediction: Leveraging Local Spatiotemporal Public Records to Inform Action](https://mlsanity.com/paper/2401.16440/) (2024)
- [Street-Level AI: Are Large Language Models Ready for Real-World Judgments?](https://mlsanity.com/paper/2508.08193/) (2025)
- [Who Pays the RENT? Implications of Spatial Inequality for Prediction-Based Allocation Policies](https://mlsanity.com/paper/2508.08573/) (2025)
- [Can LLMs Rank? A Tale of Triads and Triage](https://mlsanity.com/paper/2606.30412/) (2026)

## Abstract (arXiv)

Street-level bureaucrats interact directly with people on behalf of government agencies to perform a wide range of functions, including, for example, administering social services and policing. A key feature of street-level bureaucracy is that the civil servants, while tasked with implementing agency policy, are also granted significant discretion in how they choose to apply that policy in individual cases. Using that discretion could be beneficial, as it allows for exceptions to policies based on human interactions and evaluations, but it could also allow biases and inequities to seep into important domains of societal resource allocation. In this paper, we use machine learning techniques to understand street-level bureaucrats' behavior. We leverage a rich dataset that combines demographic and other information on households with information on which homelessness interventions they were assigned during a period when assignments were not formulaic. We find that caseworker decisions in this time are highly predictable overall, and some, but not all of this predictivity can be captured by simple decision rules. We theorize that the decisions not captured by the simple decision rules can be considered applications of caseworker discretion. These discretionary decisions are far from random in both the characteristics of such households and in terms of the outcomes of the decisions. Caseworkers typically only apply discretion to households that would be considered less vulnerable. When they do apply discretion to assign households to more intensive interventions, the marginal benefits to those households are significantly higher than would be expected if the households were chosen at random; there is no similar reduction in marginal benefit to households that are discretionarily allocated less intensive interventions, suggesting that caseworkers are improving outcomes using their knowledge.