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Generative AI, particularly Large Language Models (LLMs), has revolutionized human-computer interaction by enabling the generation of nuanced, human-like text. This presents new opportunities, especially in enhancing explainability for AI systems like recommender systems, a crucial factor for fostering user trust and engagement. LLM-powered AI-Chatbots can be leveraged to provide personalized explanations for recommendations. Although users often find these chatbot explanations helpful, they may not fully comprehend the content. Our research focuses on assessing how well users comprehend these explanations and identifying gaps in understanding. We also explore the key behavioral differences between users who effectively understand AI-generated explanations and those who do not. We designed a three-phase user study with 17 participants to explore these dynamics. The findings indicate that the clarity and usefulness of the explanations are contingent on the user asking relevant follow-up questions and having a motivation to learn. Comprehension also varies significantly based on users’ educational backgrounds. 

This paper addresses a gap in the AI governance literature in understanding collaboration between national governments and tribal nations in governing AI systems for emergency management. This conceptual work develops and presents a governance design framework for accountable AI systems to fill the knowledge gap by drawing from the fields of public administration, information systems, indigenous studies, and emergency management. This framework situates the governance framework in a cross-sovereignty historical, legal, and policy contexts. It captures the multi-level features and embeddedness of governance structures, including the levels of collaborative governance structure, software system governance rules, and technical software system design. The focal governance dynamics involve the collaborative process in the bi-directional relationship between governance rules and technical design for accountability and the feedback loop. The framework highlights the importance of multi-level and process considerations in designing accountable AI systems. Productive future research avenues include empirical investigation and resulting refinement of the framework and analytical rigor employing institutional grammar. 

Medical financial literacy is essential to make smart decisions in healthcare settings and prevent unanticipated financial hardships. Existing literature has shown that young adults often struggle to understand information associated with health insurance and the financial planning necessary for health-related costs. AI-driven chatbots are emerging as educational tools that have the potential to address this issue. This exploratory study examined an AI chatbot aimed at enhancing medical financial literacy among high school students. Participants engaged with the chatbot’s responses to medical financial questions while also rating the clarity, ease of use, trustworthiness, and educational value of the chatbot engagement. Our experiment results supported that the chatbot increased students’ understanding of the financial aspect of healthcare - 76.9 percent of students reported a high degree of understanding, 80.8 percent rated the chatbot’s responses as clear, and 73.1 percent reported they would recommend it to a peer. The responses indicated that students found the chatbot helpful, but suggested that interactive features be added and/or real-world finance features be incorporated into the chatbot. 

The advancement of generative AI, involving the utilization of large language models (LLMs) like ChatGPT to assess public opinion and sentiment, has become increasingly prevalent. However, this upsurge in usage raises significant questions about the transparency and interpretability of the predictions made by these LLM Models. Hence, this paper explores the imperative of ensuring transparency in the application of ChatGPT for public sentiment analysis. To tackle these challenges, we propose using a lexicon-based model as a surrogate to approximate both global and local predictions. Through case studies, we demonstrate how transparency mechanisms, bolstered by the lexicon-based model, can be seamlessly integrated into ChatGPT’s deployment for sentiment analysis. Drawing on the results of our study, we further discuss the implications for future research involving the utilization of LLMs in governmental functions, policymaking, and public engagement. 

Tribal governments bear an uneven burden in the face of escalating disaster risks, climate change, and environmental degradation, primarily due to their deeply entrenched ties to the environment and its resources. Regrettably, accessing vital information and evidence to secure adequate funding or support poses difficulties for enrolled tribal members and their lands. In response to these challenges, this paper collaborates with tribal nations to co-design intelligent disaster management systems using AI chatbots and drone technologies. The primary objective is to explore how tribal governments perceive and experience these emerging technologies in the realm of disaster reporting practices. This paper presents participatory design studies. First, we interviewed seasoned first-line emergency managers and hosted an in-person design workshop to introduce theEmergency Reporterchatbot. Second, we organized a follow-up design workshop on tribal land to deliberate the utilization of drones within their community. Through qualitative analysis, we unveiled key themes surrounding integrating these emergency technologies within the jurisdiction of tribal governments. The findings disclosed substantial backing from tribal governments and their tribal members for the proposed technologies. Moreover, we delved into the potential of chatbots and drones to empower tribal governments in disaster management, safeguard their sovereignty, and facilitate collaboration with other agencies.