AI-assisted Decision-making and Consumers' Trust in AI Recommendations

AI Aversion: Do People Trust and Accept Artificial Intelligence Risk Calculator Recommendations?

The findings support the AI aversion hypothesis and suggest that there needs to be more work to identify how best to explain AI-based calculators to foster trust and comfort more specifically.

Designed to be trusted: anthropomorphism in AI-assisted business decisions

Communicating Uncertainty in AI-Based Decision Support Systems: A Comparative Study of Numerical and Visual Representations

AI-based Decision Support Systems (AI-DSS) are increasingly recognized for their significance in professional environments. A key challenge in human-AI interactions is effectively communicating the uncertainty inherent in AI recommendations, as this can influence performance outcomes. Various methods exist for representing uncertainty, primarily through numerical data or visual cues. While users often favor numerical probabilities for their perceived precision, these figures can be difficult to interpret. Conversely, visual representations may enhance understanding but tend to be less accepted by users. The existing literature lacks clear conclusions regarding the impact of these communication designs on user performance and cognitive load. This research examines the effects of two forms of uncertainty communication—numerical (decimal numbers) and visual (traffic light system)—on user performance and cognitive load. An online experimental study was conducted with 104 participants assigned randomly to either condition within an AI-supported customer service context. Participants responded to support request emails using AI-ranked response modules while retaining decision-making authority. Each participant engaged with ten vignettes and completed questionnaires measuring task load afterward; performance was assessed based on correctly answered vignettes. Results indicated no significant differences in task load between groups. However, notable variations in performance emerged when systems made errors, influenced by the communication design used. These findings suggest that effective uncertainty communication strategies may vary based on context and audience, offering valuable insights for designing AI-DSS.

Linguistic Uncertainty Markers for Trust Calibration in AI-Assisted Decision-Making

While participants rated hedged and unhedged AI as equally trustworthy and likely to be correct, they were significantly less likely to follow hedged advice in a binary choice, and how linguistic markers can be used to calibrate user reliance to model certainty is discussed.

An investigation about AI in Business (marketing): a matter of trust

    Computer science includes artificial intelligence (AI) as a field. It entails creating computer programs to carry out operations that would typically require intelligence from humans. This study explores the role of Artificial Intelligence (AI) in marketing and consumer behavior, investigating its benefits, challenges, impact on consumer trust, and ethical considerations. this study is in the Kurdistan Region-Iraq. The survey was in three cities (Erbil, Sulaymaniyah and Halabja). The participants we dealt with were over 18 years old, approximately 145 respondents were recruited who were recruited through an adaptive questionnaire. and the level of knowledge of the participants includes individuals with expertise in electronics, IT, computers, and technology updates in business. the research design included quantitative data collection techniques. The results showed that using AI in marketing has a number of advantages, including improved efficiency in processing and interpreting consumer data and more accuracy in forecasting customer behavior. But it also presents serious obstacles, such as worries about data privacy, losing one's work, and being overly dependent on technology. Furthermore, concerns of accountability, justice, and openness were noted as ethical implications related to the application of AI in marketing.  

Human Perception of Trust in AI-Assisted Communication Systems

Artificial Intelligence (AI) has transformed modern communication systems by enabling intelligent interactions, automated content generation, personalized recommendations, real-time translation, and conversational support. The widespread adoption of AI in areas such as social media, healthcare, education, customer service, and enterprise communication depends largely on user trust. Trust in AI communication systems is influenced by factors such as transparency, reliability, explainability, privacy, fairness, and accuracy. However, concerns regarding bias, misinformation, data privacy, and lack of explainability can reduce user confidence. This study examines technological, psychological, and social factors affecting trust in AI-based communication systems. The findings reveal that transparency, reliability, privacy protection, and explainability are critical for building long-term user trust. The study concludes that trust is a multidimensional concept involving technical performance, ethical considerations, and user experience, highlighting the need for trustworthy AI systems to support effective and responsible digital communication.