A previous study on the use of the Empath library in the prediction of Service Level Agreements (SLA) reveals the quality levels required for meaningful interaction between government customer service officers and citizens. On the other hand, past implementation of the Citizen Question-Answer system (CQAS), a type of Question-Answer model, suggests that such models if put in place can empower government customer service officers to reply faster and better with recommended answers. This study builds upon the research outcomes from both arenas of studies and introduces an innovative system design that allows the officers to incorporate the outputs from Empath X SLA predictor and CQAS (a type of Question Answer model) as critical inputs to ChatGPT engine, known as AI Based Citizen Question-Answer Recommender (ACQAR).Empath X SLA predictor anticipates the expected service response time based on citizens’ emotional state. These valuable inputs coupled with the recommended answer provided by the CQAS will serve as prompt inputs to ChatGPT to craft contextually aware responses. This ensures that the final response considers the citizen’s emotional needs, expected service timeline, and recommended answers from official government documents.While the full-scale deployment of this pilot system, ACQAR, is pending, this paper presents a comprehensive blueprint for governments seeking to modernize citizen service delivery. By fusing sentiment analysis, SLA prediction, question-answer models, and ChatGPT, this system design aims to revolutionize government-citizen interactions, delivering more empathetic, efficient, and tailored responses, while not violating SLA.This paper serves as a foundational step towards the practical development and implementation of an intelligent system (ACQAR) that holds the potential to significantly enhance citizen satisfaction, foster trust in government services, and strengthen overall government-citizen relationships.

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.

Service Level Agreement (SLA) plays a significant role in the relationship between citizens and the government. It stipulates the quality levels required for the meaningful interaction between the two parties. Most SLA predictive models consider end-to-end duration and frequency of failed service requests as model inputs with little research on the analysis of textual details of the service request. This is an issue for government bodies as the latter do not just want to meet SLA, but also be proactive by knowing the citizens before assisting them. Inclusion of textual data potentially answer to this requirement of knowing the citizen before the officer tries to meet SLA. In this paper, we attempt to enrich SLA predictive process by analysing the textual data contained in the service requests. Based on a dataset of 800k case records from a customer service centre based in Singapore, we use text analytics to derive features from the dataset, which will be included with other commonly used variables in the prediction of SLA. We further explore the use of the Empath library to provide a categorical outcome that is more beneficial for the customer service officers to understand the citizen, than a numerical outcome. Based on our experiments, we observe that a predictive model built via logistic regression performs the best with an accuracy of 75%. This result remains valid when Empath categories are included as an input variable. This paper adds to the body of research work done in citizen service by proposing an SLA predictive model that incorporates lexical features from textual data to facilitate proactive citizen service delivery.