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

TL;DR

This study explores how AI, especially LLMs, impacts administrative burdens in benefits systems like SNAP, revealing both alleviation and new challenges related to trust and costs.

Contribution

It introduces a framework for understanding trust-burden dynamics in LLM-assisted benefits administration and highlights the importance of trust calibration and information disclosure.

Findings

AI can reduce traditional administrative burdens.

New learning, compliance, and psychological costs emerge with AI use.

Trust in AI's competence, integrity, and benevolence influences burden perception.

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.