USING LARGE LANGUAGE MODELS FOR ARMCHAIR AUDITORS

Abstract

Armchair auditors are citizens who use open data to investigate and monitor government activities, typically using analytics and other approaches. Armchair auditors provide a valuable role in holding governments and organizations accountable. This paper investigates the potential use of large language models (LLM) to support armchair auditor analyzes of different governmental entities. Unfortunately, the literature, prior to the development of LLM suggested several challenges for armchair auditors. However, the analysis in this paper suggests that LLM can provide substantial data and analytic process support for armchair auditors mitigating issues such as, providing guidelines for analyses, guiding users to appropriate communities, suggesting potential data availability opportunities, doing analysis and other issues. As part of an approach to unifying armchair auditor searches, this paper also suggests a prompt library designed to support, standardize and promote best practice analyzes among armchair auditors. In addition to these issues, this paper also analyzes emerging ethical issues associated with armchair auditors and their use of open data and LLMs. Finally, this paper extends the activity theory model to account for LLMs.