Research on how to take advantage of Augmented Reality and Virtual Reality applications and technologies in the domain of manufacturing has brought forward a great number of concepts, prototypes, and working systems. Although comprehensive surveys have taken account of the state of the art, the design space of industrial augmented and virtual reality keeps diversifying. We propose a visual approach towards assessing this space and present an interactive, community-driven tool which supports interested researchers and practitioners in gaining an overview of the aforementioned design space. Using such a framework we collected and classified relevant publications in terms of application areas and technology platforms. This tool shall facilitate initial research activities as well as the identification of research opportunities. Thus, we lay the groundwork, forthcoming workshops and discussions shall address the refinement.
Explanations in Human-AI Interaction are communicated to human decision makers through interfaces. Yet, it is not clear what consequences the exact representation of such explanations as part of decision support systems (DSS) and working on machine learning (ML) models has on human decision making. We observe a need for research methods that allow for measuring the effect different eXplainable AI (XAI) interface designs have on people's decision making. In this paper, we argue for adopting research approaches from decision theory for HCI research on XAI interface design. We outline how we used estimation tasks in human-grounded design research in order to introduce a method and measurement for collecting evidence on XAI interface effects. To this end, we investigated representations of LIME explanations in an estimation task online study as proof-of-concept for our proposal.
CCS CONCEPTS• Human-centered computing → User interface design; Visualization design and evaluation methods; • Computing methodologies → Machine learning; Artificial intelligence.
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