## 1 Introduction  
The domain of informatics is not immune to user dissatisfaction, with complaints frequently stemming from a multitude of factors that can broadly be categorized as usability impediments, system availability issues, accessibility barriers, and deficiencies in information clarity and presentation. Historically, addressing these giveances has largely relied on manual categorization processes coupled with keyword-based filtering techniques, approaches that are often resource-intensive, prone to human error, and limited in their ability to discern nuanced patterns and latent relationships within the complaint data [1]. In recent years, the burgeoning field of artificial intelligence has garnered significant attention within the realm of e-government applications, giving rise to innovative solutions such as AI-powered chatbots designed to enhance citizen engagement, smart decision support systems aimed at optimizing resource allocation, and predictive analytics tools engineered to forecast citizen needs and proactively address potential issues [2]. However, despite the growing recognition of AI's transformative potential within the public sector, a notable gap persists in the application of AI techniques to the analysis of log file patterns for the purpose of preemptively identifying and mitigating the underlying causes of user dissatisfaction within informatics systems. The digitization of data is crucial for AI adoption in public administration, enabling functions like administrative burden reduction and cost savings by moving away from paper records [3]. Proactively addressing citizen dissatisfaction before it becomes a formal complaint can improve public service delivery, enhance administrative efficiency, and build trust in government. AI can automate functions like benefits processing and compliance, and use predictive analytics to optimize resource allocation and workflow [4]. By leveraging machine learning algorithms to analyze vast quantities of log file data, it becomes possible to identify subtle patterns and anomalies that may indicate impending system failures, usability bottlenecks, or accessibility challenges [5]. Such preemptive insights can then be used to trigger automated remediation processes, such as server load balancing, software patching, or the dynamic adjustment of user interface elements, thereby minimizing the likelihood of user complaints and ensuring a seamless user experience. AI is useful in recognizing patterns of security-related activities or data security rules [6]. These AI-driven interventions can also be personalized to cater to the specific needs and preferences of individual users, further enhancing user satisfaction and promoting greater adoption of e-government services. Furthermore, AI has been shown to bolster governmental services through strong data utilization, the prediction of public needs, and the application of data-driven encryption techniques, enabling careful access decision-making and ensuring that personal data is accessible only to authorized parties, preventing privacy breaches and data abuse [7]. The ethical considerations surrounding the deployment of AI in e-government applications, particularly those involving the analysis of user data, cannot be overlooked [8; 9]. Moreover, it is imperative to establish robust data governance frameworks that prioritize transparency, accountability, and user control over data collection and usage practices. These frameworks should incorporate mechanisms for obtaining informed consent from users, ensuring data anonymization and pseudonymization techniques are employed to protect user privacy, and providing users with the ability to access, rectify, and erase their personal data [10; 11]. Additionally, it is crucial to address potential biases in AI algorithms that could lead to discriminatory outcomes or unfair treatment of certain user groups [12]. Algorithmic bias can happen when algorithms are trained with biased data; such bias may produce unfair outcomes. This issue has serious ethical implications.