AI Trust Reshaping Administrative Burdens: Understanding Trust-Burden Dynamics in LLM-Assisted Benefits Systems

2025-05-28

AI Summary

This study examines the dual impact of large language models (LLMs) on administrative burden in the U.S. Supplemental Nutrition Assistance Program (SNAP) application process—alleviating traditional procedural burdens while introducing novel learning, compliance, and psychological costs. Drawing on in-depth interview data from 41 million low-income applicants and integrating trust theory with the administrative burden framework, we develop and empirically validate a novel three-dimensional “competence–integrity–benevolence” trust model, revealing a nonlinear relationship between trust and perceived burden: excessive trust intensifies latent burdens. We introduce the principle of “trust calibration,” advocating evidence-based disclosure mechanisms to optimize AI-enabled public service design. Our findings provide empirical grounding and actionable guidance for advancing fairness and accessibility in algorithmic governance. (149 words)

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: AI for Accessibility
• Application Domains: Humanities & Computational Social Science

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Supplemental Nutrition Assistance Program (SNAP) is an essential benefit support system provided by the US administration to 41 million federally determined low-income applicants. Through interviews with such applicants across a diverse set of experiences with the SNAP system, our findings reveal that new AI technologies like LLMs can alleviate traditional burdens but also introduce new burdens. We introduce new types of learning, compliance, and psychological costs that transform the administrative burden on applicants. We also identify how trust in AI across three dimensions--competence, integrity, and benevolence--is perceived to reduce administrative burdens, which may stem from unintended and untoward overt trust in the system. We discuss calibrating appropriate levels of user trust in LLM-based administrative systems, mitigating newly introduced burdens. In particular, our findings suggest that evidence-based information disclosure is necessary in benefits administration and propose directions for future research on trust-burden dynamics in AI-assisted administration systems.

Problem

• AI technologies alleviate but introduce new administrative burdens
• Trust in AI reduces burdens but risks overt trust
• Calibrating user trust mitigates new burdens in AI systems

Innovation

• LLMs alleviate and introduce administrative burdens
• Trust in AI reduces burdens across three dimensions
• Evidence-based disclosure mitigates new burdens