Why cities need economic intelligenceThe economies of Europe’s cities are changingfast, and it is not easy to predict which segmentsof the local economy will grow and which oneswill decline. Yet, cities must make decisions as towhere to invest, and face a number of questionsthat are difficultto answer:Where dowe putour bets? Should we go for biotech, ICT, or anyother sector that may have growth potential?Do we want to attract large foreign companies,or rather support our local indigenous smallerfirms, ormustwe promotethestart-up scene?Or is it better not to go for any particularindustry but just improve the quality of lifein the city, hoping that this will help to retainskilled people and attract high tech firms?
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In het boek komen 40 experts aan het woord, die in duidelijke taal uitleggen wat AI is, en welke vragen, uitdagingen en kansen de technologie met zich meebrengt.
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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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People tend to be hesitant toward algorithmic tools, and this aversion potentially affects how innovations in artificial intelligence (AI) are effectively implemented. Explanatory mechanisms for aversion are based on individual or structural issues but often lack reflection on real-world contexts. Our study addresses this gap through a mixed-method approach, analyzing seven cases of AI deployment and their public reception on social media and in news articles. Using the Contextual Integrity framework, we argue that most often it is not the AI technology that is perceived as problematic, but that processes related to transparency, consent, and lack of influence by individuals raise aversion. Future research into aversion should acknowledge that technologies cannot be extricated from their contexts if they aim to understand public perceptions of AI innovation.
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In the book, 40 experts speak, who explain in clear language what AI is, and what questions, challenges and opportunities the technology brings.
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Ambient intelligence technologies are a means to support ageing-in-place by monitoring clients in the home. In this study, monitoring is applied for the purpose of raising an alarm in an emergency situation, and thereby, providing an increased sense of safety and security. Apart from these technological solutions, there are numerous environmental interventions in the home environment that can support people to age-in-place. The aim of this study was to investigate the needs and motives, related to ageing-in-place, of the respondents receiving ambient intelligence technologies, and to investigate whether, and how, these technologies contributed to aspects of ageing-in-place. This paper presents the results of a qualitative study comprised of interviews and observations of technology and environmental interventions in the home environment among 18 community-dwelling older adults with a complex demand for care.
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The healthcare sector has been confronted with rapidly rising healthcare costs and a shortage of medical staff. At the same time, the field of Artificial Intelligence (AI) has emerged as a promising area of research, offering potential benefits for healthcare. Despite the potential of AI to support healthcare, its widespread implementation, especially in healthcare, remains limited. One possible factor contributing to that is the lack of trust in AI algorithms among healthcare professionals. Previous studies have indicated that explainability plays a crucial role in establishing trust in AI systems. This study aims to explore trust in AI and its connection to explainability in a medical setting. A rapid review was conducted to provide an overview of the existing knowledge and research on trust and explainability. Building upon these insights, a dashboard interface was developed to present the output of an AI-based decision-support tool along with explanatory information, with the aim of enhancing explainability of the AI for healthcare professionals. To investigate the impact of the dashboard and its explanations on healthcare professionals, an exploratory case study was conducted. The study encompassed an assessment of participants’ trust in the AI system, their perception of its explainability, as well as their evaluations of perceived ease of use and perceived usefulness. The initial findings from the case study indicate a positive correlation between perceived explainability and trust in the AI system. Our preliminary findings suggest that enhancing the explainability of AI systems could increase trust among healthcare professionals. This may contribute to an increased acceptance and adoption of AI in healthcare. However, a more elaborate experiment with the dashboard is essential.
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The concept of business sustainability has been investigated, reviewed, and criticized by a plethora of scholars. What constitutes the essence of business sustainability—and its relationship to the actual state of our planet—is still an integral part of the discourse on business-society relations. Recently, Dyllick and Muff (Organization & Environment, 29:156–174, 2016) have reviewed literature in order to uncover what constitutes ‘true’ business sustainability, explaining the apparent absence of a coupling between corporate sustainability initiatives and the state of the planet and explore how this coupling can be strengthened. As such, the authors provide many relevant pointers for answering the question: when is business truly sustainable? This paper aims to respond both critically and constructively to Dyllick and Muff’s article by addressing three points: the somewhat confusing conception of what actually comprises ‘true’ business sustainability, the authors’ choice not to address the underlying economic model and the model of consumer behavior, and the types of sustainability intelligence that, in our view, business needs to develop to truly become a force for spurring sustainable development. We use the Sustainable Development Goals (SDGs) as a case in point to illustrate our argument. In doing so, this paper aims to add to a firm-centered conceptualization of the business-society interface in a constructive way to stimulate further discourse on the concept, and to make a theoretical contribution with respect to coupling mechanisms in the realm of business sustainability.
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The phenomena of urbanization and climate change interact with the growing number of older people living in cities. One of the effects of climate change is an increased riverine flooding hazard, and when floods occur this has a severe impact on human lives and comes with vast economic losses. Flood resilience management procedures should be supported by a combination of complex social and environmental vulnerability assessments. Therefore, new methodologies and tools should be developed for this purpose. One way to achieve such inclusive procedures is by incorporating a social vulnerability evaluation methodology for environmental and flood resilience assessment. These are illustrated for application in the Polish city of Wrocław. Socio-environmental vulnerability mapping, based on spatial analyses using the poverty risk index, data on the ageing population, as well as the distribution of the areas vulnerable to floods, was conducted with use of a location intelligence system combining Geographic Information System (GIS) and Business Intelligence (BI) tools. The new methodology allows for the identification of areas populated by social groups that are particularly vulnerable to the negative effects of flooding. C 2018 SETAC Original Publication: Integr Environ Assess Manag 2018;14:592–597. DOI: https://doi.org/10.1002/ieam.4077
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Background: Adverse outcome pathway (AOP) networks are versatile tools in toxicology and risk assessment that capture and visualize mechanisms driving toxicity originating from various data sources. They share a common structure consisting of a set of molecular initiating events and key events, connected by key event relationships, leading to the actual adverse outcome. AOP networks are to be considered living documents that should be frequently updated by feeding in new data. Such iterative optimization exercises are typically done manually, which not only is a time-consuming effort, but also bears the risk of overlooking critical data. The present study introduces a novel approach for AOP network optimization of a previously published AOP network on chemical-induced cholestasis using artificial intelligence to facilitate automated data collection followed by subsequent quantitative confidence assessment of molecular initiating events, key events, and key event relationships. Methods: Artificial intelligence-assisted data collection was performed by means of the free web platform Sysrev. Confidence levels of the tailored Bradford-Hill criteria were quantified for the purpose of weight-of-evidence assessment of the optimized AOP network. Scores were calculated for biological plausibility, empirical evidence, and essentiality, and were integrated into a total key event relationship confidence value. The optimized AOP network was visualized using Cytoscape with the node size representing the incidence of the key event and the edge size indicating the total confidence in the key event relationship. Results: This resulted in the identification of 38 and 135 unique key events and key event relationships, respectively. Transporter changes was the key event with the highest incidence, and formed the most confident key event relationship with the adverse outcome, cholestasis. Other important key events present in the AOP network include: nuclear receptor changes, intracellular bile acid accumulation, bile acid synthesis changes, oxidative stress, inflammation and apoptosis. Conclusions: This process led to the creation of an extensively informative AOP network focused on chemical-induced cholestasis. This optimized AOP network may serve as a mechanistic compass for the development of a battery of in vitro assays to reliably predict chemical-induced cholestatic injury.
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