Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2023

Abstract

This paper explores ChatGPT’s potential in aiding government agencies, drawing from a case study based on a government agency in Singapore. While ChatGPT’s text generation abilities offer promise, it brings inherent challenges, including data opacity, potential misinformation, and occasional errors. These issues are especially critical in government decision-making.Public administration’s core values of transparency and accountability magnify these concerns. Ensuring AI alignment with these principles is imperative, given the potential repercussions on policy outcomes and citizen trust.AI explainability plays a central role in ChatGPT’s adoption within government agencies. To address these concerns, we propose strategies like prompt engineering, data governance, and the adoption of interpretability tools such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). These tools aid in understanding and enhancing ChatGPT’s decision-making processes.This paper underscores the urgency for government agencies to adopt a proactive stance by proposing a 4-Steps framework completed with potential measures to enhance ChatGPT’s explainability within the specific context of public administration. Collaborative efforts between AI practitioners and public administrators are essential for striking an equilibrium between the capabilities of ChatGPT and the unique demands of government operations, ultimately ensuring a responsible integration of ChatGPT into public administration processes.

Keywords

ChatGPT, AI explainability, government, SHAP, LIME

Discipline

Artificial Intelligence and Robotics | Public Affairs, Public Policy and Public Administration

Research Areas

Software and Cyber-Physical Systems

Publication

2023 IEEE International Conference on Big Data (BigData): Sorrento, December 15-18: Proceedings

First Page

5852

Last Page

5856

ISBN

9798350324457

Identifier

10.1109/BigData59044.2023.10386797

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1109/BigData59044.2023.10386797

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