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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In today's world, understanding different viewpoints is key for societal cohesion and progress. Robots have the potential to provide aid in discussing tough topics like ethnicity and gender. However, comparably to humans, the appearance of a robot can trigger inherent prejudices. This study delves into the interplay between robot appearance and decision-making in ethical dilemmas. Employing a Furhat robot that can change faces in an instant, we looked at how robot appearance affects decision-making and the perception of the robot itself. Pairs of participants were invited to discuss a dilemma presented by a robot, covering sensitive topics of ethnicity or gender. The robot either adopted a first-person or third-person perspective and altered its appearance accordingly. Following the explanation, participants were encouraged to discuss their choice of action in the dilemma situation. We did not find significant influences of robot appearance or dilemma topic on perceived anthropomorphism, animacy, likeability, or intelligence of the robot, partly in line with previous research. However, several participants hearing the dilemma from a first-person perspective changed their opinion because of the robot's appearance. Future work can expand with different measures such as engagement, in order to shed light on the intricate dynamics of human-robot interaction, emphasizing the need for thoughtful consideration in designing robot appearances to promote unbiased engagement in discussions of societal significance
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With artificial intelligence (AI) systems entering our working and leisure environments with increasing adaptation and learning capabilities, new opportunities arise for developing hybrid (human-AI) intelligence (HI) systems, comprising new ways of collaboration. However, there is not yet a structured way of specifying design solutions of collaboration for hybrid intelligence (HI) systems and there is a lack of best practices shared across application domains. We address this gap by investigating the generalization of specific design solutions into design patterns that can be shared and applied in different contexts. We present a human-centered bottom-up approach for the specification of design solutions and their abstraction into team design patterns. We apply the proposed approach for 4 concrete HI use cases and show the successful extraction of team design patterns that are generalizable, providing re-usable design components across various domains. This work advances previous research on team design patterns and designing applications of HI systems.
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Technology in general, and assistive technology in particular, is considered to be a promising opportunity to address the challenges of an aging population. Nevertheless, in health care, technology is not as widely used as could be expected. In this chapter, an overview is given of theories and models that help to understand this phenomenon. First, the design of (assistive) technologies will be addressed and the importance of human-centered design in the development of new assistive devices will be discussed. Also theories and models are addressed about technology acceptance in general. Specific attention will be given to technology acceptance in healthcare professionals, and the implementation of technology within healthcare organizations. The chapter will be based on the state of the art of scientific literature and will be illustrated with examples from our research in daily practice considering the different perspectives of involved stakeholders.
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Walking meetings are a promising way to reduce unhealthy sedentary behavior at the office. Some aspects of walking meetings are however hard to assess using traditional research approaches that do not account well for the embodied experience of walking meetings. We conducted a series of 16 bodystorming sessions, featuring unusual walking meeting situations to engage participants (N=45) in a reflective experience. After each bodystorming, participants completed three tasks: a body map, an empathy map, and a rating of workload using the NASA-TLX scale. These embodied explorations provide insights on key themes related to walking meetings: material and tools, physical and mental demand, connection with the environment, social dynamics, and privacy. We discuss the role of technology and opportunities for technology-mediated walking meetings. We draw implications for the design of walking meeting technologies or services to account for embodied experiences, and the individual, social, and environmental factors at play.
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Office well-being aims to explore and support a healthy, balanced and active work style in office environments. Recent work on tangible user interfaces has started to explore the role of physical, tangible interfaces as active interventions to explore how to tackle problems such as inactive work and lifestyles, and increasingly sedentary behaviours. We identify a fragmented research landscape on tangible Office well-being interventions, missing the relationship between interventions, data, design strategies, and outcomes, and behaviour change techniques. Based on the analysis of 40 papers, we identify 7 classifications in tangible Office well-being interventions and analyse the intervention based on their role and foundation in behaviour change. Based on the analysis, we present design considerations for the development of future tangible Office well-being design interventions and present an overview of the current field and future research into tangible Office well-being interventions to design for a healthier and active office environment.
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Maintaining the child-robot relationship after a significant break, such as a holiday, is an important step for developing sustainable social robots for education. We ran a four-session user study (n = 113 children) that included a nine-month break between the third and fourth session. During the study, participants practiced math with the help of a social robot math tutor. We found that social personalization is an effective strategy to better sustain the child-robot relationship than the absence of social personalization. To become reacquainted after the long break, the robot summarizes a few pieces of information it had stored about the child. This gives children a feeling of being remembered, which is a key contributor to the effectiveness of social personalization. Enabling the robot to refer to information previously shared by the child is another key contributor to social personalization. Conditional for its effectiveness, however, is that children notice these memory references. Finally, although we found that children's interest in the tutoring content is related to relationship formation, personalizing the topics did not lead to more interest in the content. It seems likely that not all of the memory information that was used to personalize the content was up-to-date or socially relevant.
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In a multi-sensory environment, supported with embedded computer technology, the system can capture and interpret what the users are doing and assist or collaborate with the users in real-time. Such an environment should be aware of users intentions, tasks and feelings, and allow people to interact with the environment in a natural way: by moving, pointing and gesturing. In this paper we propose an architecture for such a smart environment consisting of three modules.
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One aspect of a responsible application of Artificial Intelligence (AI) is ensuring that the operation and outputs of an AI system are understandable for non-technical users, who need to consider its recommendations in their decision making. The importance of explainable AI (XAI) is widely acknowledged; however, its practical implementation is not straightforward. In particular, it is still unclear what the requirements are of non-technical users from explanations, i.e. what makes an explanation meaningful. In this paper, we synthesize insights on meaningful explanations from a literature study and two use cases in the financial sector. We identified 30 components of meaningfulness in XAI literature. In addition, we report three themes associated with explanation needs that were central to the users in our use cases, but are not prominently described in literature: actionability, coherent narratives and context. Our results highlight the importance of narrowing the gap between theoretical and applied responsible AI.
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The Office Jungle is an experimental office environment designed to make offices more "wild". Through this demonstration and associated design vision, we make a first attempt to reflect on and to define what characterizes wildness and how it could empower people in more playful and active lifestyles, particularly in the workplace. In our understanding, wildness is not an exclusive property of nature, but rather a condition that can be designed for. How wildness can be designed is described here in a set of design principles called "Design for Wildness", inspired by the work of Gibson. The Office Jungle, a large geodesic sphere of 2 meters in diameter, is part and parcel of these design principles and can be used as a tool to design other wild environments. Such environments could benefit people working in the office, many of whom have been suffering the consequences of a sedentary lifestyle.
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