Vision Paper: Advancing of AI explainability for the use of ChatGPT in government agencies: Proposal of a 4-step framework - LEE, Hui Shan (Institutional Knowledge at Singapore Management University, 2023)
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.