This extended abstract introduces the work of the Netherlands AI Media and Democracy lab, especially focusing on the research performed from an AI/computer science perspective at CWI, the Netherlands National Research Center for Mathematics and Computer Science in Amsterdam. We first provide an overview of the general aims and set-up of the lab, and then focuses in on the research areas of the 3 research groups at CWI, outlining there are of research and expected research contributions in the areas between AI and media & democracy
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Complex interventions are criticized for being a “black box”, which makes it difficult to determine why they succeed or fail. Recently, nine proactive primary-care programs aiming to prevent functional decline in older adults showed inconclusive effects. The aim of this study was to systematically unravel, compare, and synthesize the development and evaluation of nine primary-care programs within a controlled trial to further improve the development and evaluation of complex interventions. A systematic overview of all written data on the nine proactive primary-care programs was conducted using a validated item list.
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Background: Although physical activity (PA) has positive effects on health and well-being, physical inactivity is a worldwide problem. Mobile health interventions have been shown to be effective in promoting PA. Personalizing persuasive strategies improves intervention success and can be conducted using machine learning (ML). For PA, several studies have addressed personalized persuasive strategies without ML, whereas others have included personalization using ML without focusing on persuasive strategies. An overview of studies discussing ML to personalize persuasive strategies in PA-promoting interventions and corresponding categorizations could be helpful for such interventions to be designed in the future but is still missing. Objective: First, we aimed to provide an overview of implemented ML techniques to personalize persuasive strategies in mobile health interventions promoting PA. Moreover, we aimed to present a categorization overview as a starting point for applying ML techniques in this field. Methods: A scoping review was conducted based on the framework by Arksey and O’Malley and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria. Scopus, Web of Science, and PubMed were searched for studies that included ML to personalize persuasive strategies in interventions promoting PA. Papers were screened using the ASReview software. From the included papers, categorized by the research project they belonged to, we extracted data regarding general study information, target group, PA intervention, implemented technology, and study details. On the basis of the analysis of these data, a categorization overview was given. Results: In total, 40 papers belonging to 27 different projects were included. These papers could be categorized in 4 groups based on their dimension of personalization. Then, for each dimension, 1 or 2 persuasive strategy categories were found together with a type of ML. The overview resulted in a categorization consisting of 3 levels: dimension of personalization, persuasive strategy, and type of ML. When personalizing the timing of the messages, most projects implemented reinforcement learning to personalize the timing of reminders and supervised learning (SL) to personalize the timing of feedback, monitoring, and goal-setting messages. Regarding the content of the messages, most projects implemented SL to personalize PA suggestions and feedback or educational messages. For personalizing PA suggestions, SL can be implemented either alone or combined with a recommender system. Finally, reinforcement learning was mostly used to personalize the type of feedback messages. Conclusions: The overview of all implemented persuasive strategies and their corresponding ML methods is insightful for this interdisciplinary field. Moreover, it led to a categorization overview that provides insights into the design and development of personalized persuasive strategies to promote PA. In future papers, the categorization overview might be expanded with additional layers to specify ML methods or additional dimensions of personalization and persuasive strategies.
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Energy transition is key to achieving a sustainable future. In this transition, an often neglected pillar is raising awareness and educating youth on the benefits, complexities, and urgency of renewable energy supply and energy efficiency. The Master Energy for Society, and particularly the course “Society in Transition”, aims at providing a first overview on the urgency and complexities of the energy transition. However, educating on the energy transition brings challenges: it is a complex topic to understand for students, especially when they have diverse backgrounds. In the last years we have seen a growing interest in the use of gamification approaches in higher institutions. While most practices have been related to digital gaming approaches, there is a new trend: escape rooms. The intended output and proposed innovation is therefore the development and application of an escape room on energy transition to increase knowledge and raise motivation among our students by addressing both hard and soft skills in an innovative and original way. This project is interdisciplinary, multi-disciplinary and transdisciplinary due to the complexity of the topic; it consists of three different stages, including evaluation, and requires the involvement of students and colleagues from the master program. We are confident that this proposed innovation can lead to an improvement, based on relevant literature and previous experiences in other institutions, and has the potential to be successfully implemented in other higher education institutions in The Netherlands.
Everyone has the right to participate in society to the best of their ability. This right also applies to people with a visual impairment, in combination with a severe or profound intellectual and possibly motor disability (VISPIMD). However, due to their limitations, for their participation these people are often highly dependent on those around them, such as family members andhealthcare professionals. They determine how people with VISPIMD participate and to what extent. To optimize this support, they must have a good understanding of what people with disabilities can still do with their remaining vision.It is currently difficult to gain insight into the visual abilities of people with disabilities, especially those with VISPIMD. As a professional said, "Everything we can think of or develop to assess the functional vision of this vulnerable group will help improve our understanding and thus our ability to support them. Now, we are more or less guessing about what they can see.Moreover, what little we know about their vision is hard to communicate to other professionals”. Therefore, there is a need for methods that can provide insight into the functional vision of people with VISPIMD, in order to predict their options in daily life situations. This is crucial knowledge to ensure that these people can participate in society to their fullest extent.What makes it so difficult to get this insight at the moment? Visual impairments can be caused by a range of eye or brain disorders and can manifest in various ways. While we understand fairly well how low vision affects a person's abilities on relatively simple visual tasks, it is much more difficult to predict this in more complex dynamic everyday situations such asfinding your way or moving around during daily activities. This is because, among other things, conventional ophthalmic tests provide little information about what people can do with their remaining vision in everyday life (i.e., their functional vision).An additional problem in assessing vision in people with intellectual disabilities is that many conventional tests are difficult to perform or are too fatiguing, resulting in either no or the wrong information. In addition to their visual impairment, there is also a very serious intellectual disability (possibly combined with a motor impairment), which makes it even more complex to assesstheir functional vision. Due to the interplay between their visual, intellectual, and motor disabilities, it is almost impossible to determine whether persons are unable to perform an activity because they do not see it, do not notice it, do not understand it, cannot communicate about it, or are not able to move their head towards the stimulus due to motor disabilities.Although an expert professional can make a reasonable estimate of the functional possibilities through long-term and careful observation, the time and correct measurement data are usually lacking to find out the required information. So far, it is insufficiently clear what people with VZEVMB provoke to see and what they see exactly.Our goal with this project is to improve the understanding of the visual capabilities of people with VISPIMD. This then makes it possible to also improve the support for participation of the target group. We want to achieve this goal by developing and, in pilot form, testing a new combination of measurement and analysis methods - primarily based on eye movement registration -to determine the functional vision of people with VISPIMD. Our goal is to systematically determine what someone is responding to (“what”), where it may be (“where”), and how much time that response will take (“when”). When developing methods, we take the possibilities and preferences of the person in question as a starting point in relation to the technological possibilities.Because existing technological methods were originally developed for a different purpose, this partly requires adaptation to the possibilities of the target group.The concrete end product of our pilot will be a manual with an overview of available technological methods (as well as the methods themselves) for assessing functional vision, linked to the specific characteristics of the target group in the cognitive, motor area: 'Given that a client has this (estimated) combination of limitations (cognitive, motor and attention, time in whichsomeone can concentrate), the order of assessments is as follows:' followed by a description of the methods. We will also report on our findings in a workshop for professionals, a Dutch-language article and at least two scientific articles. This project is executed in the line: “I am seen; with all my strengths and limitations”. During the project, we closely collaborate with relevant stakeholders, i.e. the professionals with specific expertise working with the target group, family members of the persons with VISPIMD, and persons experiencing a visual impairment (‘experience experts’).
The focus of the research is 'Automated Analysis of Human Performance Data'. The three interconnected main components are (i)Human Performance (ii) Monitoring Human Performance and (iii) Automated Data Analysis . Human Performance is both the process and result of the person interacting with context to engage in tasks, whereas the performance range is determined by the interaction between the person and the context. Cheap and reliable wearable sensors allow for gathering large amounts of data, which is very useful for understanding, and possibly predicting, the performance of the user. Given the amount of data generated by such sensors, manual analysis becomes infeasible; tools should be devised for performing automated analysis looking for patterns, features, and anomalies. Such tools can help transform wearable sensors into reliable high resolution devices and help experts analyse wearable sensor data in the context of human performance, and use it for diagnosis and intervention purposes. Shyr and Spisic describe Automated Data Analysis as follows: Automated data analysis provides a systematic process of inspecting, cleaning, transforming, and modelling data with the goal of discovering useful information, suggesting conclusions and supporting decision making for further analysis. Their philosophy is to do the tedious part of the work automatically, and allow experts to focus on performing their research and applying their domain knowledge. However, automated data analysis means that the system has to teach itself to interpret interim results and do iterations. Knuth stated: Science is knowledge which we understand so well that we can teach it to a computer; and if we don't fully understand something, it is an art to deal with it.[Knuth, 1974]. The knowledge on Human Performance and its Monitoring is to be 'taught' to the system. To be able to construct automated analysis systems, an overview of the essential processes and components of these systems is needed.Knuth Since the notion of an algorithm or a computer program provides us with an extremely useful test for the depth of our knowledge about any given subject, the process of going from an art to a science means that we learn how to automate something.
