In modern life, the application of artificial intelligence (AI) has promoted the implementation of data-driven algorithms in high-stakes domains, such as healthcare. However, it is becoming increasingly challenging for humans to understand the working and reasoning of these complex and opaque algorithms. For AI to support essential decisions in these domains, specific ethical issues need to be addressed to prevent the misinterpretation of AI, which may have severe consequences for humans. However, little research has been published on guidelines that systematically addresses ethical issues when AI techniques are applied in healthcare. In this systematic literature review, we aimed to provide an overview of ethical concerns and related strategies that are currently identified when applying AI in healthcare. The review, which followed the PRISMA guidelines, revealed 12 main ethical issues: justice and fairness, freedom and autonomy, privacy, transparency, patient safety and cyber security, trust, beneficence, responsibility, solidarity, sustainability, dignity, and conflicts. In addition to these 12 main ethical issues, we derived 19 ethical sub-issues and associated strategies from the literature.

Living labs are an extremely attractive open innovation landscape for collaborative research and development activities targeting the complexity of today’s societal challenges. However, although there is plenty of support for collaboration, we still lack clear guidelines to direct transdisciplinary stakeholder networks of academics and practitioners through collaboration processes in the living lab ecosystem. In other words, we lack answers to the question of “how to collaborate?” In the present paper we propose a conceptual framework defining relevant stages to initiate and facilitate transdisciplinary collaboration processes. We base our framework on collaboration challenges described in the literature, specifically the need for stakeholder alignment, as well as challenges experienced in practice, which we report through exploratory case studies. In the proposed conceptual framework, we advocate the application of co-creation methods, both at the level of the living lab (macro) and in projects (meso) within the living lab, in order to define, with all involved parties and stakeholders, the scope and strategy of the living lab and to facilitate stakeholder alignment. Additionally, we integrate an iterative approach and a feedback loop in order to account for the dynamic nature of the collaboration process and to enable reflection and evaluation.

Physical inactivity and chronic stress at work increase the risks of developing metabolic disorders, mental illnesses, and musculoskeletal injuries, threatening office workers’ physical and psychological well-being. Although several guidelines and interventions have been developed to prevent theses subhealth issues, their effectiveness and health benefits are largely limited when they cannot match workday contexts. This paper presents LightSit, a health-promoting system that helps people reduce physically inactive behaviors and manage chronic stress at work. LightSit comprises a sensor mat that can be embedded into an office chair for measuring a user’s sitting posture and heart rate variability and a lighting display that is integrated into a monitor stand to present information unobtrusively, facilitating fitness and relaxation exercises during microbreaks. Following the showroom approach, we evaluated LightSit during a public exhibition at Dutch Design Week 2018. During the eight days of the exhibition, we observed more than 500 sessions of experiences with healthy microbreaks using our prototype. Semistructured interviews were conducted with 50 participants who had office-based jobs and had experienced LightSit. Our qualitative findings indicated the potential benefits of LightSit in facilitating health-promoting behaviors during office work. Based on the insights learned from this study, we discuss the implications for future designs of interactive health-promoting systems.

Artificial Intelligence (AI) has expanded in a diverse context, it infiltrates our social lives and is a critical part of algorithmic decision-making. Adopting AI technology, especially AI-enabled design, by end users who are non-AI experts is still limited. The incomprehensible, untransparent decision-making and difficulty of using AI become obstacles which prevent these end users to adopt AI technology. How to design the user experience (UX) based on AI technologies is an interesting topic to explore.This paper investigates how non-AI-expert end users can be engaged in the design process of an AI-enabled application by using a framework called Smart Service Blueprint Scape (SSBS), which aims to establish a bridge between UX and AI systems by mapping and translating AI decisions based on UX. A Dutch mobility service called ‘stUmobiel ’ was taken as a design case study. The goal is to design a reservation platform with stUmobiel end users. Co-creating with case users and assuring them to understand the decision-making and service provisional process of the AI-enabled design is crucial to promote users’ adoption. Furthermore, the concern of AI ethics also arises in the design process and should be discussed in a broader sense.

It is increasingly recognized that community-based interventions for active ageing are more lasting and effective, yet the tools and methods for developing these interventions are lacking. This study investigates how to co-design community-based active ageing with older adults via the development of a toolkit to support this goal. Rapid reviews were conducted to understand (i) the effective behavioural change techniques for older adults, (ii) how to co-design with older adults for community-based interventions, and (iii) how to design tools for behaviour change that are easy to use. These reviews served as the foundation for developing a toolkit to support the co-design of community-based active ageing, which was evaluated during an interdisciplinary hackathon with older adults. Quantitative data from the surveys suggested that the confidence levels of students in developing interventions for health behaviour change and in co-designing with older adults increased after the hackathon, and the enjoyment of participating in the hackathon and of using the toolkit were statistically significant factors influencing this increase. Qualitative data from interviews and observations revealed how the toolkit was (un)used by the participants and what aspects of the toolkit can be improved. We encourage future researchers and practitioners to apply and adapt our research findings to the communities of older adults that they are working with.