The effective completion of tasks in collaborative systems hinges on task-specific trust evaluations of potential devices for distributed collaboration. Due to independent operation of devices involved, dynamic evolution of their mutual relationships, and complex situation-related impact on trust evaluation, effectively assessing devices' trust for collaborator selection is challenging. To overcome this challenge, we propose a semantic chain-of-trust model implemented with agentic AI and hypergraphs for supporting effective collaborator selection. We first introduce a concept of semantic trust, specifically designed to assess collaborators along multiple semantic dimensions for a more accurate representation of their trustworthiness. To facilitate intelligent evaluation, an agentic AI system is deployed on each device, empowering it to autonomously perform necessary operations, including device state detection, trust-related data collection, semantic extraction, task-specific resource evaluation, to derive a semantic trust representation for each collaborator. In addition, each device leverages a hypergraph to dynamically manage potential collaborators according to different levels of semantic trust, enabling fast one-hop collaborator selection. Furthermore, adjacent trusted devices autonomously form a chain through the hypergraph structure, supporting multi-hop collaborator selection. Experimental results demonstrate that the proposed semantic chain-of-trust achieves 100\% accuracy in trust evaluation based on historical collaborations, enabling intelligent, resource-efficient, and precise collaborator selection.

We introduce and analyse active learning markets as a way to purchase labels, in situations where analysts aim to acquire additional data to improve model fitting, or to better train models for predictive analytics applications. This comes in contrast to the many proposals that already exist to purchase features and examples. By originally formalising the market clearing as an optimisation problem, we integrate budget constraints and improvement thresholds into the label acquisition process. We focus on a single-buyer-multiple-seller setup and propose the use of two active learning strategies (variance based and query-by-committee based), paired with distinct pricing mechanisms. They are compared to a benchmark random sampling approach. The proposed strategies are validated on real-world datasets from two critical application domains: real estate pricing and energy forecasting. Results demonstrate the robustness of our approach, consistently achieving superior performance with fewer labels acquired compared to conventional methods. Our proposal comprises an easy-to-implement practical solution for optimising data acquisition in resource-constrained environments.

Large Language Model (LLM) agents require personal information for personalization in order to better act on users' behalf in daily tasks, but this raises privacy concerns and a personalization-privacy dilemma. Agent's autonomy introduces both risks and opportunities, yet its effects remain unclear. To better understand this, we conducted a 3$\times$3 between-subjects experiment ($N=450$) to study how agent's autonomy level and personalization influence users' privacy concerns, trust and willingness to use, as well as the underlying psychological processes. We find that personalization without considering users' privacy preferences increases privacy concerns and decreases trust and willingness to use. Autonomy moderates these effects: Intermediate autonomy flattens the impact of personalization compared to No- and Full autonomy conditions. Our results suggest that rather than aiming for perfect model alignment in output generation, balancing autonomy of agent's action and user control offers a promising path to mitigate the personalization-privacy dilemma.

For premium consumer products, pricing strategy is not about a single number, but about understanding the perceived monetary value of the features that justify a higher cost. This paper proposes a robust methodology to deconstruct a product's price into the tangible value of its constituent parts. We employ Bayesian Hierarchical Conjoint Analysis, a sophisticated statistical technique, to solve this high-stakes business problem using the Apple iPhone as a universally recognizable case study. We first simulate a realistic choice based conjoint survey where consumers choose between different hypothetical iPhone configurations. We then develop a Bayesian Hierarchical Logit Model to infer consumer preferences from this choice data. The core innovation of our model is its ability to directly estimate the Willingness-to-Pay (WTP) in dollars for specific feature upgrades, such as a "Pro" camera system or increased storage. Our results demonstrate that the model successfully recovers the true, underlying feature valuations from noisy data, providing not just a point estimate but a full posterior probability distribution for the dollar value of each feature. This work provides a powerful, practical framework for data-driven product design and pricing strategy, enabling businesses to make more intelligent decisions about which features to build and how to price them.

In human society, trust is an essential component of social attitude that helps build and maintain long-term, healthy relationships which creates a strong foundation for cooperation, enabling individuals to work together effectively and achieve shared goals. As many human interactions occur through electronic means such as using mobile apps, the potential arises for AI systems to assist users in understanding the social state of their relationships. In this paper we investigate the ability of Large Language Models (LLMs) to reason about trust between two individuals in an environment which requires fostering trust relationships. We also assess whether LLMs are capable of inducing trust by role-playing one party in a trust based interaction and planning actions which can instil trust.

Generative AI (GenAI) chatbots are becoming increasingly integrated into virtual assistant technologies, yet their success hinges on the ability to gather meaningful user feedback to improve interaction quality, system outcomes, and overall user acceptance. Successful chatbot interactions can enable organizations to build long-term relationships with their customers and users, supporting customer loyalty and furthering the organization's goals. This study explores the impact of two distinct narratives and feedback collection mechanisms on user engagement and feedback behavior: a standard AI-focused interaction versus a hybrid intelligence (HI) framed interaction. Initial findings indicate that while small-scale survey measures allowed for no significant differences in user willingness to leave feedback, use the system, or trust the system, participants exposed to the HI narrative statistically significantly provided more detailed feedback. These initial findings offer insights into designing effective feedback systems for GenAI virtual assistants, balancing user effort with system improvement potential.

Digital deliberation has expanded democratic participation, yet challenges remain. This includes processing information at scale, moderating discussions, fact-checking, or attracting people to participate. Recent advances in artificial intelligence (AI) offer potential solutions, but public perceptions of AI's role in deliberation remain underexplored. Beyond efficiency, democratic deliberation is about voice and recognition. If AI is integrated into deliberation, public trust, acceptance, and willingness to participate may be affected. We conducted a preregistered survey experiment with a representative sample in Germany (n=1850) to examine how information about AI-enabled deliberation influences willingness to participate and perceptions of deliberative quality. Respondents were randomly assigned to treatments that provided them information about deliberative tasks facilitated by either AI or humans. Our findings reveal a significant AI-penalty. Participants were less willing to engage in AI-facilitated deliberation and rated its quality lower than human-led formats. These effects were moderated by individual predispositions. Perceptions of AI's societal benefits and anthropomorphization of AI showed positive interaction effects on people's interest to participate in AI-enabled deliberative formats and positive quality assessments, while AI risk assessments showed negative interactions with information about AI-enabled deliberation. These results suggest AI-enabled deliberation faces substantial public skepticism, potentially even introducing a new deliberative divide. Unlike traditional participation gaps based on education or demographics, this divide is shaped by attitudes toward AI. As democratic engagement increasingly moves online, ensuring AI's role in deliberation does not discourage participation or deepen inequalities will be a key challenge for future research and policy.

Pronunciation instruction in foreign language classrooms has often been an overlooked area of focus. With the widespread adoption of Artificial Intelligence (AI) and its potential benefits, investigating how AI is utilized in pronunciation teaching and understanding the beliefs of teachers about this tool is essential for improving learning outcomes. This study aims to examine how AI use for pronunciation instruction varies across different demographic and professional factors among teachers, and how these factors, including AI use, influence the beliefs of teachers about AI. The study involved 117 English as a Foreign Language (EFL) in-service teachers working in Cyprus, who completed an online survey designed to assess their beliefs about the effectiveness of AI, its drawbacks, and their willingness to integrate AI into their teaching practices. The results revealed that teachers were significantly more likely to agree on the perceived effectiveness of AI and their willingness to adopt it, compared to their concerns about its use. Furthermore, teachers working in higher education and adult education, as well as those who had received more extensive training, reported using AI more frequently in their teaching. Teachers who utilized AI more often expressed stronger agreement with its effectiveness, while those who had received more training were less likely to express concerns about its integration. Given the limited training that many teachers currently receive, these findings demonstrate the need for tailored training sessions that address the specific needs and concerns of educators, ultimately fostering the adoption of AI in pronunciation instruction.

Artificial Intelligence (AI) systems are frequently employed in online services to provide personalized experiences to users based on large collections of data. However, AI systems can be designed in different ways, with black-box AI systems appearing as complex data-processing engines and white-box AI systems appearing as fully transparent data-processors. As such, it is reasonable to assume that these different design choices also affect user perception and thus their willingness to share data. To this end, we conducted a pre-registered, scenario-based online experiment with 240 participants and investigated how transparent and non-transparent data-processing entities influenced data-sharing intentions. Surprisingly, our results revealed no significant difference in willingness to share data across entities, challenging the notion that transparency increases data-sharing willingness. Furthermore, we found that a general attitude of trust towards AI has a significant positive influence, especially in the transparent AI condition, whereas privacy concerns did not significantly affect data-sharing decisions.

User willingness is a crucial element in the sales talk process that affects the achievement of the salesperson's or sales system's objectives. Despite the importance of user willingness, to the best of our knowledge, no previous study has addressed the development of automated sales talk dialogue systems that explicitly consider user willingness. A major barrier is the lack of sales talk datasets with reliable user willingness data. Thus, in this study, we developed a user willingness-aware sales talk collection by leveraging the ecological validity concept, which is discussed in the field of human-computer interaction. Our approach focused on three types of user willingness essential in real sales interactions. We created a dialogue environment that closely resembles real-world scenarios to elicit natural user willingness, with participants evaluating their willingness at the utterance level from multiple perspectives. We analyzed the collected data to gain insights into practical user willingness-aware sales talk strategies. In addition, as a practical application of the constructed dataset, we developed and evaluated a sales dialogue system aimed at enhancing the user's intent to purchase.