The increasing integration of AI-driven interfaces into public service channels has catalyzed a vibrant discourse on the interplay between technological innovation and the traditional values of public governance. This discussion invites a critical exploration of how emerging chatbot architectures can be aligned with ethical principles and resilient public sector practices. While there is research assessing the potential benefits of integrating chatbots in service delivery, existing evaluation approaches often lack specificity to the unique context of public administration, failing to adequately balance technical performance with crucial ethical considerations, safety requirements, and core public service principles like transparency, fairness, and accountability. This research addresses this critical gap by developing and applying a structured evaluation framework specifically designed for assessing diverse chatbot architectures within the public sector . The methodology offers actionable insights to guide the selection and implementation of chatbot solutions that enhance citizen engagement, streamline government services, and uphold key public service values. A key contribution is the introduction of fifteen pre- assessed evaluation criteria, encompassing areas such as input understanding, error handling, legal compliance, safety, and personalization, which are applied to four distinct chatbot architectures. Our findings indicate that while no single architecture is universally optimal, hybrid retrieval-augmented generation (RAG) systems emerge as the most balanced approach, effectively mitigating the risks of pure generative models while retaining their adaptability. Ultimately, this work provides actionable guidance for policymakers and researchers, supporting informed decisions on the responsible use of chatbots and emphasizing the critical balance between innovation and public trust.
Παπαδόπουλος et al. (2025) studied this question.