Automate, Assist, Avoid: Caseworkers’ Perspectives on Applying Large Language Model-Based Assistance in Public Sector Decision-Making Processes

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Authors: Karolina Drobotowicz, Johanna Ylipulli, Uttishta Sreerama Varanasi, Heidi S Mäkitalo
Cited by: 1
References: 57

Abstract

Large language models (LLMs) are being introduced into the public sector – for example, to assist caseworkers in making decisions on citizens’ cases. However, there is limited knowledge of how LLM tools can be used effectively in this complex task, including legal and cultural variables. This qualitative study foregrounds the perspectives of caseworkers from a Finnish public institution to dismantle their decision-making process and to build nuanced understanding on which sub-tasks of the process could benefit from the use of LLMs and how. To suggest meaningful uses for LLMs in the public sector, decision-making needs to be understood as a process that consists of several parts and that varies considerably in different contexts. We contribute to the fields of human–computer interaction and public administration by detailing the decision-making process of caseworkers and their perspectives on technological assistance, to suggest practical integration possibilities for LLM tools.

Fields of Study & Subjects

E-Government and Public Services Information Systems Theories and Implementation Multi-Agent Systems and Negotiation

Publication details

Journal / source
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Pages
1–16
Publication date
13 April 2026
Language
EN