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Google wants to “help computers ‘see’ our world”, and one of their ways of battling how current AI and machine learning systems perpetuate biases is to introduce a more inclusive scale of skin tone, the ‘Monk Skin Tone Scale’.
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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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Scientific research on the quality of face detection systems keeps finding the same result: no matter how, when, and with which system testing is done, every time it is found that faces of people with a darker skin tone are not detected as well as the faces of people with a lighter skin tone.
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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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The process of making adaptive and responsive wearables on the scale of the body hasoften been a process where designers use off-the-shelf parts or hand-crafted electronics to fabricategarments. However, recent research has shown the importance of emergence in the process of making.Second Skins is a multistakeholder exploration into the creation of those garments where the designersand engineers work together throughout the design process so that opportunities and challengesemerge with all stakeholders present in the process. This research serves as a case study into thecreation of adaptive caring garments for sustainable wardrobes from a multistakeholder designteam. The team created a garment that can customize the colors, patterns, structures, and otherproperties dynamically. A reflection on the multi-stakeholder process unpacks the process to explorethe challenges and opportunities in adaptable e-textiles.
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Both because of the shortcomings of existing risk assessment methodologies, as well as newly available tools to predict hazard and risk with machine learning approaches, there has been an emerging emphasis on probabilistic risk assessment. Increasingly sophisticated AI models can be applied to a plethora of exposure and hazard data to obtain not only predictions for particular endpoints but also to estimate the uncertainty of the risk assessment outcome. This provides the basis for a shift from deterministic to more probabilistic approaches but comes at the cost of an increased complexity of the process as it requires more resources and human expertise. There are still challenges to overcome before a probabilistic paradigm is fully embraced by regulators. Based on an earlier white paper (Maertens et al., 2022), a workshop discussed the prospects, challenges and path forward for implementing such AI-based probabilistic hazard assessment. Moving forward, we will see the transition from categorized into probabilistic and dose-dependent hazard outcomes, the application of internal thresholds of toxicological concern for data-poor substances, the acknowledgement of user-friendly open-source software, a rise in the expertise of toxicologists required to understand and interpret artificial intelligence models, and the honest communication of uncertainty in risk assessment to the public.
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Recent advancements in mobile sensing and wearable technologies create new opportunities to improve our understanding of how people experience their environment. This understanding can inform urban design decisions. Currently, an important urban design issue is the adaptation of infrastructure to increasing cycle and e-bike use. Using data collected from 12 cyclists on a cycle highway between two municipalities in The Netherlands, we coupled location and wearable emotion data at a high spatiotemporal resolution to model and examine relationships between cyclists' emotional arousal (operationalized as skin conductance responses) and visual stimuli from the environment (operationalized as extent of visible land cover type). We specifically took a within-participants multilevel modeling approach to determine relationships between different types of viewable land cover area and emotional arousal, while controlling for speed, direction, distance to roads, and directional change. Surprisingly, our model suggests ride segments with views of larger natural, recreational, agricultural, and forested areas were more emotionally arousing for participants. Conversely, segments with views of larger developed areas were less arousing. The presented methodological framework, spatial-emotional analyses, and findings from multilevel modeling provide new opportunities for spatial, data-driven approaches to portable sensing and urban planning research. Furthermore, our findings have implications for design of infrastructure to optimize cycling experiences.
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High consumption of carbohydrates is linked to metabolic syndrome, possibly via the endogenous formation of advanced glycated end-products. Many Dutch elementary school children have a carbohydrate intake of >130g/day, the estimated minimum requirement. In this observational study, 126 Dutch elementary school children (5-12y of age) from two schools differing in frequency of gym lessons (2 or 5 times a week) were included. In all participants, height, weight, waist circumference, autofluorescence of skin glycated end-products (AGE-score), sports activity and carbohydrate consumption were recorded once. Sports activities in leisure time differentiated participants in ‘sportsmen’ and ‘non-sportsmen’. Carbohydrate intake and AGE score were positively associated in non-sportsmen (p<0.003), but negatively in sportsmen (p<0.002). In sportsmen, but not in non-sportsmen (p>0.50), a positive association was found (p<0.002) between carbohydrate intake and subject age. The intake of total carbohydrate and carbohydrates from juices and soft drinks was lower (p<0.001) at the Wassenberg School relative to the Alexander School. Based on waist to height ratio, >95% of the children had normal fat mass. No correlations were found between waist to height ratio or BMI and carbohydrate intake. Waist to height ratio was positively associated with BMI (p<0.001)) and subject age (p<0.001). Of all principal parameters, AGE score is most affected by being sportsmen or not (p<0.001). This study indicates that an increased intake of carbohydrates can be counteracted by sufficient physical activity (>2.5 hours per week). This implies that skin autofluorescence is a fast and non-invasive method to screen children for life style.
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This chapter describes the growing influence of point-of-care diagnostics (POCD) on the daily lives of citizens, their immediate families, and healthcare providers. With a view to the future, the most important contemporary developments in this field are discussed, such as noninvasive sensor technology in the diagnostic process, practical examples of point-of-care diagnostics (POCD), including the quantify-self movement and infrared technology. Cost-effectiveness, adoption of POCD, and the contribution of POCD innovations to self-management and health literacy are also discussed. Developments in which deep learning and artificial intelligence are used to make the diagnostic results more reliable are also conferred, such as the development of point-of-care Internet diagnostics. The discussion of professional advice dilemma’s in POCD, the patient’s appreciation of POCD, and ethical and philosophical considerations conclude this chapter.
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