Author: engeladmin

Author: Vanneste, Bart S.; Puranam, Phanish Description: Modern artificial intelligence (AI) technologies based on deep learning architectures are often perceived as agentic to varying degrees–typically, as more agentic than other technologies but less agentic than humans. We theorize how different levels of perceived agency of AI affect human trust in AI. We do so by investigating three causal pathways. First, an AI (and its designer) perceived as more agentic will be seen as more capable, and therefore will be perceived as more trustworthy. Second, the more the AI is perceived…

Author: Molina, Maria D.; Sundar, S. Shyam Description: When evaluating automated systems, some users apply the “positive machine heuristic” (i.e. machines are more accurate and precise than humans), whereas others apply the “negative machine heuristic” (i.e. machines lack the ability to make nuanced subjective judgments), but we do not know much about the characteristics that predict whether a user would apply the positive or negative machine heuristic. We conducted a study in the context of content moderation and discovered that individual differences relating to trust in humans, fear of artificial…

Author: Cabrero-Daniel, Beatriz; Cabrero, Andrea Sanagustin Description: Natural Language Generation tools, such as chatbots that can generate human-like conversational text, are becoming more common both for personal and professional use. However, there are concerns about their trustworthiness and ethical implications. The paper addresses the problem of understanding how different users (e.g., linguists, engineers) perceive and adopt these tools and their perception of machine-generated text quality. It also discusses the perceived advantages and limitations of Natural Language Generation tools, as well as users’ beliefs on governance strategies. The main findings of…