In this paper, we explore the design of web-based advice robots to enhance users' confidence in acting upon the provided advice. Drawing from research on algorithm acceptance and explainable AI, we hypothesise four design principles that may encourage interactivity and exploration, thus fostering users' confidence to act. Through a value-oriented prototype experiment and valueoriented semi-structured interviews, we tested these principles, confirming three of them and identifying an additional principle. The four resulting principles: (1) put context questions and resulting advice on one page and allow live, iterative exploration, (2) use action or change oriented questions to adjust the input parameters, (3) actively offer alternative scenarios based on counterfactuals, and (4) show all options instead of only the recommended one(s), appear to contribute to the values of agency and trust. Our study integrates the Design Science Research approach with a Value Sensitive Design approach.
Physical activity is crucial in human life, whether in everyday activities or elite sports. It is important to maintain or improve physical performance, which depends on various factors such as the amount of physical activity, the capability, and the capacity of the individual. In daily life, it is significant to be physically active to maintain good health, intense exercise is not necessary, as simple daily activities contribute enough. In sports, it is essential to balance capacity, workload, and recovery to prevent performance decline or injury.With the introduction of wearable technology, it has become easier to monitor and analyse physical activity and performance data in daily life and sports. However, extracting personalised insights and predictions from the vast and complex data available is still a challenge.The study identified four main problems in data analytics related to physical activity and performance: limited personalised prediction due to data constraints, vast data complexity, need for sensitive performance measures, overly simplified models, and missing influential variables. We proposed end investigated potential solutions for each issue. These solutions involve leveraging personalised data from wearables, combining sensitive performance measures with various machine learning algorithms, incorporating causal modelling, and addressing the absence of influential variables in the data.Personalised data, machine learning, sensitive performance measures, advanced statistics, and causal modelling can help bridge the data analytics gap in understanding physical activity and performance. The research findings pave the way for more informed interventions and provide a foundation for future studies to further reduce this gap.
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De uitdagingen voor de (toekomstige) professionals zijn groot. Voor de innovatie van juridische dienstverlening en de morele dillema’s binnen de juridische beroepspraktijk, zijn hbo’ers nodig die verder kunnen kijken dan de regels en de toepassing ervan; mensen die altijd de mens blijven zien en horen en kunnen reflecteren op hun professioneel handelen. Er zijn meer toegankelijke en rechtvaardige oplossingen nodig die zorgen voor de toegang tot recht. Dat vergt behalve het kunnen inleven in mensen ook maatwerk, praktische wijsheid en creatief denken. Waarbij praktische wijsheid staat voor het zoeken naar antwoorden in concrete situaties waarvoor geen standaardantwoorden uit regelgeving, leerboeken en protocollen te vinden zijn
