Titlebook: Artificial Intelligence in HCI; First International Helmut Degen,Lauren Reinerman-Jones Conference proceedings 2020 Springer Nature Switze

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Ludo Van der Heyden,Quy Nguyen Huylibration” as an apt design goal– user trust should be at an appropriate level given a system’s reliability. One factor in user trust is the degree to which a system is perceived as humanlike, or anthropomorphic. However, relevant prior work does not explicitly characterize trust appropriateness, an

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Ludo Van der Heyden,Quy Nguyen Huys, for instance in medical diagnostics. While the algorithms’ complexity is a reason for their increased performance, it also leads to the ‘black box’ problem, consequently decreasing trust towards AI. In this regard, “Explainable Artificial Intelligence” (XAI) allows to open that black box and to i

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The Impact of Family Business on Societyrs to help define potential Human-Machine Interface (HCI) and Human-Machine Teaming (HMT) models for human-centered machine translation systems. The role of the machine, in this context, is to be an AI-based agent serving as a real-time partner. The questions we ask are that if we have such an agent

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https://doi.org/10.1007/978-1-349-13306-2dia data of an online news organization. After generating the personas, we aggregate each personas’ YouTube comments and predict the “Big Five” personality traits of each persona from the comments pertaining to that persona. For this, we develop a deep learning classifier using three publicly availa

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Social Perspectives in Child Abuse in regard to perceived trustworthiness and observability. As the amount of processes automated by artificial intelligence (AI) increases, so does the need to investigate users’ perception. Previous research on explainable AI (XAI) tends to focus on technological optimization. The limited amount of

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