Virtual reality (VR) has enjoyed a steep growth in awareness in society in recent years and is considered a promising tool for the design and enhancement of experiences. However, as research and use cases in the hospitality context are expanding rapidly, it is crucial to define a clearer research direction that aligns the number of scattered studies across various fields. It is time to overcome the boundaries of the technological dimension and explore methods for purposeful VR design. This research note calls for more user-centric VR studies and developments to define the future direction of VR implementations in the hospitality and tourism industry. To achieve this, the authors recommend the use of design methodology with a focus on the first steps in the design process to clearly identify and understand customers' needs and desires independent of VR technology.
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Explainable Artificial Intelligence (XAI) aims to provide insights into the inner workings and the outputs of AI systems. Recently, there’s been growing recognition that explainability is inherently human-centric, tied to how people perceive explanations. Despite this, there is no consensus in the research community on whether user evaluation is crucial in XAI, and if so, what exactly needs to be evaluated and how. This systematic literature review addresses this gap by providing a detailed overview of the current state of affairs in human-centered XAI evaluation. We reviewed 73 papers across various domains where XAI was evaluated with users. These studies assessed what makes an explanation “good” from a user’s perspective, i.e., what makes an explanation meaningful to a user of an AI system. We identified 30 components of meaningful explanations that were evaluated in the reviewed papers and categorized them into a taxonomy of human-centered XAI evaluation, based on: (a) the contextualized quality of the explanation, (b) the contribution of the explanation to human-AI interaction, and (c) the contribution of the explanation to human- AI performance. Our analysis also revealed a lack of standardization in the methodologies applied in XAI user studies, with only 19 of the 73 papers applying an evaluation framework used by at least one other study in the sample. These inconsistencies hinder cross-study comparisons and broader insights. Our findings contribute to understanding what makes explanations meaningful to users and how to measure this, guiding the XAI community toward a more unified approach in human-centered explainability.
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Data is widely recognized as a potent catalyst for advancing healthcare effectiveness, increasing worker satisfaction, and mitigating healthcare costs. The ongoing digital transformation within the healthcare sector promises to usher in a new era of flexible patient care, seamless inter-provider communication, and data-informed healthcare practices through the application of data science. However, more often than not data lacks interoperability across different healthcare institutions and are not readily available for analysis. This inability to share data leads to a higher administrative burden for healthcare providers and introduces risks when data is missing or when delays occur. Moreover, medical researchers face similar challenges in accessing medical data due to thedifficulty of extracting data from applications, a lack of standardization, and the required data transformations before it can be used for analysis. To address these complexities, a paradigm shift towards a data-centric application landscape is essential, where data serves as the bedrock of the healthcare infrastructure and is application agnostic. In short, a modern way to think about data in general is to go from an application driven landscape to a data driven landscape, which will allow for better interoperability and innovative healthcare solutions.In the current project the research group Digital Transformation at Hanze University of Applied Sciences works together with industry partners to build an openEHR implementation for a Groningen-based mental healthcare provider.
