Street-Level AI: Are Large Language Models Ready for Real-World Judgments?
This work addresses the problem of AI readiness for high-stakes societal decision-making, such as resource allocation by street-level bureaucrats, highlighting significant inconsistencies that question naive integration.
The paper examined how well large language models (LLMs) align with human judgments and vulnerability scoring systems in homelessness resource allocation, finding that LLM prioritizations are extremely inconsistent across runs, models, and systems, though they show qualitative consistency with lay human judgments.
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 human judgments through various thought experiments or on the group fairness implications of AI judgments. However, the most immediate and likely use of AI is to help or fully replace the so-called street-level bureaucrats, the individuals deciding to allocate scarce social resources or approve benefits. There is a rich history underlying how principles of local justice determine how society decides on prioritization mechanisms in such domains. In this paper, we examine how well LLM judgments align with human judgments, as well as with socially and politically determined vulnerability scoring systems currently used in the domain of homelessness resource allocation. Crucially, we use real data on those needing services (maintaining strict confidentiality by only using local large models) to perform our analyses. We find that LLM prioritizations are extremely inconsistent in several ways: internally on different runs, between different LLMs, and between LLMs and the vulnerability scoring systems. At the same time, LLMs demonstrate qualitative consistency with lay human judgments in pairwise testing. Findings call into question the readiness of current generation AI systems for naive integration in high-stakes societal decision-making.