Robots are increasingly navigating our living environments and must navigate socially to be accepted. While existing socially aware navigation (SAN) approaches enable robots to interpret and communicate social information to navigate efficiently, safely, and in a socially acceptable manner, they often overlook potential conflicts and errors in real-world human-robot interactions. This thesis contributes to SAN by investigating how robots can adapt their inappropriate navigation behavior based on human feedback (perceived appropriateness), leading to smoother and less error-prone human-robot interactions....

In the past year, generative AI has made significant leaps, making it more accessible, helpful, and known to the broader public. This technology promises to change how we work, automating repetitive tasks, generating content, and assisting in problem-solving scenarios easier. This research explores how implementing this new technology will influence the strategic design process and how strategic designers should adapt to get the most out of the technology. As a result, a guidebook has been written, educating designers on how to use generative AI during the creative process.

The literature review gathers a thorough understanding of the strategic design process and the limitations and capabilities of current generative AI. With this understanding, different activities from the process are tested while using generative AI during the activities. From qualitative insights gained through reflections with respondents, insights are gathered on how the role of the strategic designer might change, how the strategic design process might change, and how to use generative AI effectively during the creative process.

The study found that the strategic design process will be infused with AI-stimulated creativity, resulting in potentially more innovative outcomes. To use AI effectively, it is best to use it in a hybrid approach, using AI one-on-one and taking the results gained with AI to discuss with other stakeholders without AI. Because of the limitations of generative AI, such as bias and false information, there is an increased need for human oversight during the process.

New strategic designers will be able to use generative AI as a catalyst during their workflow rather than as a way to automate the whole process. Designers should always check and iterate AI-enabled results, making them the ethical gatekeepers of the process. They are facilitators guiding other stakeholders on using generative AI to co-create with them while doing it effectively and ethically. To do so, designers' skillsets need to expand, highlighting the importance of education on using generative AI, including prompting techniques, approaches, and principles.

A separate guidebook gathers insights on how to use AI effectively, serving as a guide for designers who want to use generative AI during their creative process.

This thesis examines the role of Automated Decision-Making (ADM) systems in advancing energy justice within urban Electric Vehicle Charging Infrastructure (EVCI) in the Netherlands. Centred on distributive, procedural, and recognition justice, the study explores how ADM impacts equitable access to EV charging and fair energy distribution amid the rapid electrification of passenger mobility. While ADM promises improved grid efficiency and stability, alleviating congestion, it also risks exacerbating socio-economic disparities in access to essential charging resources and energy, potentially widening existing inequalities.

Using a mixed-method approach encompassing Geographic Information System (GIS) analysis, Scenario Development, and Q-methodology, this research examines ADM’s effects on equity, fairness, inclusivity, and transparency across socio-economic groups. GIS findings reveal disparities in EVCI accessibility between affluent and low-income neighbourhoods in Amsterdam, highlighting the need for ADM frameworks that consider socio-economic factors in energy allocation. Scenario Development projects futures where stakeholders must balance individual and community needs, illustrating the critical trade-offs required to achieve equitable energy distribution. The Q-methodology builds consensus among diverse stakeholders, underscoring transparency and procedural fairness in promoting trust and aligning ADM outcomes with public values.

This study contributes to energy justice literature by showing how ADM systems can promote equitable EVCI outcomes that are just and inclusive. Policy recommendations offer a phased roadmap: in the short term, prioritise equitable distribution of EVCI using socio-economic data; in the medium term, develop adaptive ADM models for high-demand and grid-constrained regions; and in the long term, enhance community engagement through accessible platforms. \textit{Mindful Power} envisions ADM systems for EVCI that optimise technological efficiency without sacrificing social justice, supporting a fairer, more inclusive energy transition. Ultimately, this thesis provides a structured roadmap for sustainable, community-centred urban energy solutions, ensuring that the transition leaves no one behind.

Our routine interactions with connected products and services result in the collection and indefinite storage of personal digital-trace data. These data are increasingly entangled with our lives. What we experience is scattered around multiple data points: our browsing history can account for our interests and worries, our messaging logs can account for our social relationships, and our purchase history can account for our dietary preferences. Digital-trace data is increasingly valuable for scientific research as it can offer insights into specific aspects of our daily experiences. Researchers across various disciplines have been developing methods and tools to access these data. One of these, and the focus of this thesis, is data donation. In this thesis, I develop alternative data donation approach informed by the principles of Data Feminism. This approach, called Sensitive Data Donation, aims to empower donors to know and care for their data and promotes different forms of knowledge and participation. It is directed at intimate research contexts, such as the home and the body, which are inherently private and situated spaces where it is crucial to explore and negotiate people's relationships with their data.

In this dissertation, we propose innovative approaches for assessing urban greenspace accessibility with a specific focus on factors affecting children’s access. Access to quality greenspaces in urban areas is crucial for fostering the health and well-being of children and providing spaces for recreation, socialization, and personal development. Unlike adults', children’s access to greenspaces is influenced by factors such as their daily activity patterns, levels of autonomy, and various physical and perceived barriers. While many studies in epidemiology, spatial equity, and urban planning aim to evaluate access to greenspaces for children across different urban scales and geographical contexts, they often utilize methods designed for the general population, overlooking the distinct factors affecting children's access. Our research addresses this gap by developing tailored methodologies that account for the specific needs and experiences of children in urban environments.

PROBLEM
Foundation Models are emerging as a new paradigm in AI research &
commercialisation. While this opens up possibilities for radically innovative
solutions and significant value creation, startups are challenged with finding
unique & differentiated ways to leverage the technology, while simultaneously
mitigating potentially negative consequences.
Despite the rising prevalence of machine learning (ML) in products, designers
face challenges in creating solutions that make the best use of the new
possibilities. Designers building AI products are rarely involved in problem
setting & value finding, primarily solving human-AI interaction problems.
Additionally, common human-centered, customer feedback based innovation
approaches hinder radical innovation.
Lastly, AI Ethics and responsible innovation continues to be an afterthought.
Product designers are seldom involved in mitigating the potentially negative
consequences of such products.

APPROACH
This thesis combines literature study with a "research through design"
approach to explore ways to address these problems. Working in collaboration
with a startup trying to leverage Large Language Models (LLMs) in their
products,I use empirical research to engage in the process of finding radically
innovative opportunities for using LLMs to create value for customers.
I study risk assessment practices to explore how designers can anticipate
risks in the discovery phase of product design. I then design & test tools that
can support other designers in the future.

OUTCOMES
This thesis makes multiple contributions to further the research on
technological & responsible innovation. It documents my process for finding &
analysing user insights to support the discovery of potentially radical
innovations. Additionally, I explore the process of finding value propositions
that leverage Foundation Models, and their potential risks, early in the design
process. The thesis also records how I design a process for discovering and
anticipating potential risks of harm, and how I developed a pair of canvases
and card decks to support future designers.
The thesis supports product designers in repeating these processes through
the pair of canvases and card decks. These help them to collaborate with
engineers, and contribute to the innovation & risk mitigation processes more
effectively. The discussions focus on observations & recommendations that
can further aid them.
To help designers innovate responsibly, this thesis brings the discovery of
opportunities and risks of using Foundation Models into the same
conversation. The designed deliverables and processes showcase how both
aspects of technology innovation can be tackled in similar ways.
Through all of the above, this thesis showcases the relevance of designerly
ways of thinking and doing to the fields of radical innovation, risk
management, and Foundation Model based product development.

Surveillance cameras have become increasingly prevalent in public areas worldwide, including in Amsterdam. In Amsterdam, the number and intensity of surveillance cameras in public spaces, such as streets, parks, and plazas, have reportedly increased, even in locations where they were already well-established. In addition to the growing number of cameras, they are now appearing in new contexts, such as billboards, and in new forms, such as those equipped with ANPR technology. As a result, individuals in urban areas are likely to have their images captured multiple times daily as they engage in routine activities in public spaces. While the installation of these cameras is purported to serve various purposes, such as deterring crime, prosecuting criminals retroactively, and making people feel safer, significant concerns have been raised about the potential for their irresponsible use, including concerns about the violation of individuals’ privacy, potential threat to civil liberties, and unjustified purpose of using cameras.

The current master's graduation project aims to comprehensively understand the responsible use of surveillance cameras through three phases of empirical investigations. The first phase seeks a deeper comprehension of the broader socio-technical context within which surveillance cameras are situated. Through expert interviews, two theoretical mapping representations of the context have been generated. Next, the second phase aims to comprehend the responsible use of surveillance cameras by understanding the values of key stakeholders, in which a set of value similarities and tensions among key stakeholders have been identified through a stakeholder survey study. Finally, the last phase of this project aims to develop a comprehensive set of evaluation criteria for the assessment of a broad spectrum of design concepts that pertain to the responsible use of surveillance cameras in urban public spaces.

In this report, a proposal for a sustainable last mile is given. Inner city logistics brings many problems with it regarding livability in the city; polluting vans standing in the way on the road or the sidewalk and driving too fast because of high demands of the consumer and competitive nature of carriers. Unsustainable practices are being dealt with in regulations (in the Netherlands) such as the Zero Emission Stadslogistiek. The aim of the Zero Emission Urban Logistics is to reduce emissions of CO2, NOx and particulate matter due to urban traffic to 0%. In addition, parties want to reduce noise pollution.
More sustainable transport like cargo bikes or light electric vehicles could play a role in solving these problems on the relatively short term. However, because they typically have smaller capacity and have smaller ranges than the vans that are currently used they are not the default solution for consignment in the city; distribution centres nowadays typically lie too far away from the end consignee to effectively employ these type of vehicles.
The fact that distribution centres lie so far away from the city stems from more latent problem of the last mile; its inaccessible and un-communal nature.
In this project I propose 7 ‘design activities’ in response of the aforementioned problems: cooperation, incentivisation, standardisation, decentralisation, integration (in the city), automation and consolidation. Based on these design activities I proposed three types of urban consolidation centres that facilitate smaller types of transport while at the same time considering the aforementioned design activities.

The project aims to understand the relationship between Computational Design (CD) and Industrial Design (ID) and to investigate future scenarios for CD adoption. CD can be defined as a shift in design representation and thinking, that has significant implications for the design process and its outcome. With CD, machines become active participants in the design process, producing design content in collaboration with the designer. CD allows designers to approach design problems from a different angle. Instead of focusing on a single artifact, they can define a process or system capable of generating a series of outputs exploring the design pace. The boundary conditions of this system can be associated with project requirements, environmental factors, user preferences, and the personal sensitivity of the designer. CD can increase efficiency during the design process by automating design tasks. Furthermore, CD stimulates creativity by encouraging designers to frame design problems differently and by exposing them to a large number of solutions that are sometimes beyond human imagination. The exploration of the design space depends on the generative capabilities of the algorithm used and is often unrelated to past products. Therefore, the solutions created through CD can go beyond typology, resulting in unexpected outcomes. The thesis covers CD  theoretical aspects, such as those linked to applying evolutionary processes inspired by nature to design. These theoretical notions can help to explore CD beyond the cases presented in this thesis.
The project also investigates CD methodologies. Among these, genetic algorithms stand out for optimization and design space exploration, while architected materials enable higher levels of control over geometry. The potential of CD in ID is outlined through a series of opportunities, such as the ability to: Explore a vast solution space, which can lead to new and innovative designs, augmenting creativity. Optimize technical requirements, from mechanical performances to those related to sustainability. Achieve hyper-control over geometry, which can lead to structures that can enable different mechanical properties and functions using a single material. Automate parts of the design process, which can increase efficiency and scalability. Create mass-customized products and configurators to meet the specific needs of individual users.

The first part of the thesis discusses CD’s present and past; the second part instead focuses on testing the water for possible adoption of the methodology among industrial designers. The collaboration with Accenture Industry X - Industrial Design (Formerly VanBerlo) was fundamental to outline the perspectives of industrial designers CD and in particular their view on opportunities and barriers to adopting the methodology. The research revealed a deep interest in CD: participants identified a vast range of opportunities from sustainability to using configurators for user research. However, there is a need to further explore CD and its future opportunities. The thesis concludes with a series of recommendations for making CD more accessible and usable in a professional context. This thesis intends to contribute to communicating the relevance of CD, hence spreading its use in ID.
 

KLM is the flag carrier and biggest airline in the Netherlands. The contribution to climate change of the airline industry requires urgent changes in innovation to be sustainable. At the organisation, innovation is multidisciplinary, and needs of cross-divisional activity. The innovation team is the team responsible for overseeing the innovation processes that actually take place in the different divisions.

In 2019, the innovation team started to be aware of the opportunity of innovating as an ‘ecosystem’, with frequent transfers of knowledge and cross-divisional synergies. After several efforts, the assignment of this thesis stated the need for a digital product that can interactively represent the actors involved in the different ecosystems within the company. The assignment came with an implicit set of assumptions and a research phase followed to provide better understanding of the context of the assignment.

A number of insights were gathered as a result of the research phase. First, it is not only understandable but imperative for organisations to innovate in networks given the competitiveness and complexity of the current business environment. Second, employees at the organisation, as a result of the activity of the industry, are partially focused on execution and barriers among departments have raised. Third, the culture at the organisation shows formality and structure, valuing efficiency and with productivity residing in the individual and in the respective department.

If the innovation ecosystem concept is to be supported by the innovation team, efforts should address the inefficiency of lateral collaboration. Moreover, the efforts should influence the different areas of the organisation, such as the shared values, systems or staff. Apart from the insights gathered from the research phase, this thesis developed a set of practical interventions that aim to foster collaboration while facilitating the implementation of the innovation ecosystem.

The first intervention comes after the redefinition of the original assignment. Designing a tool that represents the actors can help, but only if matched with interventions in other areas. After an iterative design process, a tool that allows the actors within an ecosystem to see others’ involvement was built using Power BI. The second intervention required a reorganisation of the knowledge-management system used for innovation. The intervention provided a new information architecture and layout for knowledge to be easily retrieved, and presumably collaboration within the ecosystem fostered. Finally, the last intervention identified a set of cultural brokers who can connect groups of employees with an innovation purpose. Studies on social networks and innovation show the brokers own a competitive advantage within the networks: they are more creative and can help bridge structural holes. Brokers can be nurtured when a cross-divisional need exists and the innovation requires to close the gap.

Finally, after developing the interventions, the innovation team is recommended to define the governance of the ecosystems in place. Defining governance will help align expectations towards collaboration and knowledge sharing from the stakeholders involved, and is a first crucial step before continuing with implementation efforts after the thesis. Once governance is defined, the organisation is provided with guidance on how to continue with implementation tasks, by starting a pilot project and including a manual of the designed tool to assist users during their learning.

Today many products exclude users from different sociodemographic groups such as ethnicity and race. Examples of these in physical products are automatic soap dispensers and airport body scanners. The digital era has appeared and there are still digital products excluding users of different ethnicities and race, for example, dating apps and algorithms used to find racism and discrimination on social media. Therefore, both physical and digital products continue to cause exclusion among users, mainly caused by the designer’s implicit biases. Yet, the faculty of Industrial Design Engineering (IDE) at TU Delft has a scarcity of resources and courses available to integrate an inclusive design perspective for its students and staff. This project aims to raise awareness for inclusivity and set an example for the type of tools needed to carry out an inclusive design perspective in the IDE faculty.

The Delft Inclusive Design (DID) platform was designed to bring together inclusivity resources available in the IDE faculty. To learn how to carry out an inclusive design approach, the DID platform offers resources and guidelines created by the IDE faculty members. These resources focus on creating awareness about inclusivity and how personal experiences shape our designs and guidelines, to carry out inclusive design practices. With a questionnaire among 13 students and interviews with 2 staff members, the DID platform proved the significance of the topic and contents of the DID platform. It also proved the student and staff interest in the topic of inclusivity, the effectiveness of the platform and its envisioned content, and the willingness of students and staff to contribute to the platform. As part of the DID platform, an awareness activity was designed for students to reflect on the influence of implicit biases in their design work. This awareness activity was tested in 3 sessions to grasp the knowledge acquired in short-term, medium-term, and long-term effect. Each session was carried out by 6 student participants and took place every 2 weeks. The outcomes show that a 3-session process is essential when creating awareness as session 1 creates new knowledge that students need to grasp, session 2 ‘reminds’ students of their learnings and by session 3 students are able to recall the information learned in session 1. Additionally, a guideline to inclusive imagery was created to aid students and staff when selecting inclusive imagery. These guidelines proved useful and provided knowledge to all 6 student participants and 4 staff participants. The awareness activity and image guideline set an example to how these can take form, in order to promote inclusive design perspectives at the IDE faculty.

In highly complex sources of data for pattern recognition, like audio, it is hard to obtain a set of information that is both extensively annotated and includes the wide variety of interfering noises that real life applications would encounter. In order to address these issues, information sharing techniques were devised, known as multi-task learning. These forms of learning algorithms learn multiple tasks at the same time, sharing numerical updates of their parameters. By doing this, a whole new amount of opportunities are opened up to mix and match tasks and datasets and the amount of applications of this are growing. However, while more and more promising results have been achieved using multi-task set-ups, there is an added amount of developmental complexity by having to deal with multiple datasets and tasks. This complexity grows signicantly the more dierences there are in the combinations. Furthermore, research requires experimentation and comparison of dierent set-ups, which is quickly complicated by the combinations, compared to single task set-ups. In order to promote research in this eld, these developmental roadblocks must be cleared up. So far however, no framework seems to aid in the development of multi-dataset, let alone multi-task set-ups. This work addresses the technical diculties in implementing, evaluating and experimenting with multi-task set-ups. By devising the multi-task set-up as a pipeline going from the raw datasets to trained and evaluated models with interchangeable parts, a framework is built that brings the development closer to single dataset, single task learning set-ups. The idea is for developers to only have to focus on implementing single pipeline parts, with the freedom to assemble and re-assemble them without having to worry about the combinatorial problems. This starts by investigating the current literature, where the elds in audio recognition and multi-task learning are analysed, specically for how and what they research and consecutively how these areas came together. Frameworks for the development of deep learning are also examined, determining which lessons to take in tackling this issue and nding the current state of aairs. This study provides the basis for the developmental software framework, CombinaTorch, built on top of PyTorch, that allows free assembly of multi-task pipelines. The framework is subsequently used to implement several set-ups described in the literature, where their comparative reduction in amount of work is assessed. In order to validate the expressiveness of the framework, another delve into the literature is made, discussing several scenarios and their implementation viability with the framework.

In our everyday activities, trackers and IoT devices are recording an increasing amount of data in the wild. It is believed that incorporating these data will contribute to more inclusive products and services as well as stimulate designers' creativity. As a result of the General Data Protection Regulation, users can now access their data and take the control of its usage. Under such circumstances, one can freely donate personal data for research purposes. Moreover, users have the opportunity to utilize their data more efficiently and to be aware of the importance of their privacy.
This project is about exploring how an effective public data visualization could be designed to encourage more people to donate personal data at the very beginning of the data collection process, with a limit on the amount of data that may be manipulated. Students who are not experts in data visualization are the target audience. And the brightspace platform is chosen since it is the simplest and most convenient way for students to receive faculty notifications. As data visualization outcomes and people's willingness to donate data vary depending on the type of data, according to the research insights, this project focuses exclusively on public photo visualizations. A series of two design iterations was conducted in order to leverage the intrinsic motivation of students to donate photos of their home working environment to be used for research purposes. This project explores motivation from the perspective of self-determination theory, and proposes criteria and design directions for designing photo visualizations for behavior change purpose.
In this study, the author identified five fundamental needs, including autonomy, competence, connectedness, purpose, and stimulation in order to evaluate data visualization's effects on the intimate data donation experience. Autonomy: Donors should feel free to choose the type of photos they want to donate. Their photos can also be used to express themselves creatively. Competence: Users should feel their photos are needed for the visualization. Relatedness: Users could feel connected with the visualization platform as they explored or interacted with it. Additionally, they believe they could build relationships by donating photos. Purpose: Users believe that donating their photos is meaningful to themselves or to researchers who require them. Stimulation: The way to ask for their donation is novel. And the entire experience with this visualization platform is enjoyable and interesting.
Through the study of three types of photo visualization, it was discovered that whether the visualization is explanation dominant or exploration dominant has an impact on users' willingness and results in varying types of photo donations. Further, design directions for effective self-determined public photo visualization in terms of information, function, and visual form were synthesized as a starting point for more future data visualization projects to motivate autonomous data donation behavior.

The Netflix documentary, ‘The Social Dilemma’, has shown that ubiquitous digital platforms such as Facebook, YouTube tend to use artificial intelligence to optimize the system for user engagement. The documentary provides an example of why metrics that optimize engagement, such as time-on-site, can be detrimental to our society. Overemphasizing current metrics resulting in manipulation, short-term concerns, and other inadvertent negative consequences.

OPTIMIZE FOR WELLBEING
Why do companies not optimize for wellbeing since it is in their best interest in the long-term to keep their users subscribed to their service? The answer is that it is easier and, in the short term, profitable to measure time on-site than wellbeing. In a 2016 TED talk, Tristan Harris provided an example: “Tinder, where instead of measuring the number of swipes left and right people did, measuring the deep, romantic, fulfilling connections people created.” Here, the existing metric, the number of swipes left and right, is easy to measure; conversely, the alternative, fulfilling romantic relationship, is not. However, the goal of a dating app should be to connect two individuals meaningfully; the number of swipes not equal to a positive relationship. This problem highlights the difficulty of translating human values into feasible metrics.

DEEPER QUESTION BEHIND ETHICAL AI
What if we want ethical AI, systems that can do good to people? What if we want AI value alignment, for instance, ensuring AI systems obey human value. According to the paper “Artificial Intelligence, Values, and Alignment.”, behind each vision for ethically-aligned AI sits a deeper question. How are we to decide which principles or objectives to encode in AI and who has the right to make these decisions?

INCLUDE STAKEHOLDERS TO REACH AI VALUE ALIGNMENT
One possible way of answering this is by including stakeholders and those most impacted in the design process and combine quantitative measures with qualitative information. Columbia professor and New York Times Chief Data Scientist Chris Wiggins stated,“Since we cannot know in advance every phenomenon users will experience, we cannot know in advance what metrics will quantify these phenomena”. In other words, we first need to understand the user’s perspective of AI experience to be able to develop suitable metrics. Thus, participatory design can be a way to translate human values into wellbeing metrics that fit the context. In this project, we focus on wellbeing as our value.

PROJECT FOCUS
To operationalize wellbeing, we need to know which aspects of wellbeing require focus by giving users a voice to inform us of their perspectives. This project will focus on developing a method that could operationalize wellbeing concept for participatory design. This methodology will be applied to sensitize end-users and allow them to share their wellbeing concern freely and meaningfully. Although the motivation of this project comes from the needs of AI for wellbeing, the project focuses on a problem that exists outside of AI alone, namely, sensitizing wellbeing. Because to be able to design an AI for wellbeing system, we first need to enable people to talk about wellbeing.

This MSc. Thesis explores the adoption process of Artificial Intelligence (AI) systems in local governmental agencies. The research uses a Design Science Research approach to explore this adoption process in the innovation adoption literature. The outcomes of this literature analysis are used to create a meaningful arrangement of determinants in a single framework. The use of this framework is demonstrated and tested through expert interviews. The results of this evaluation are used to iterate on a final framework that shows the adoption process of AI-systems in local governmental agencies.

History has taught us that not all innovation is good innovation. Just take for example the ecological devastation the DDT and the carcinogenic asbestos have caused. The advancement in new technologies like AI, IoT, autonomous robots and big data brings with it plenty of uncertainties, risks and ethical questions that need answers to if we wish to reap their benefits safely. We need these technologies to tackle some of the greater challenges of your society (like sustainability) but at the same time, we require ways to know how they should be deployed responsibly. This project focuses on the responsible deployment of technologies in the city of Amsterdam and this project has been done in collaboration with the Cities of Things Labs and the AMS institute. The project starts with an initial problem statement of “How can the “Principles for Accountable Algorithms and a Social Impact Statement for Algorithms” be used to develop a strategic framework for responsible Al innovation in Amsterdam” and to answer this question a literature review was done, Municipalities were researched, perception of the citizens were researched and in order to understand the relation of devices to the city a technology called Scan Cars was researched. Later in the project, the initial problem statement was reformulated into a change statement. The formulated change statement was “I want citizens to get well informed about what data collecting devices are doing in public spaces so that citizens can help authorities decide what is best for them” the value of transparency and the ladder of citizen participation were seen as means for achieving the above statement. The final design that was developed is a mobile application that shows citizens where the various data collecting devices are around them in the city, what actions do these devices take after they collect the data, how do the devices work and it gives citizens the opportunity to respond to these devices by either asking a question, giving feedback or participate in programs that work to improve the way of working of these devices in the city. In order to ensure technologies responsible usage in the city, it is important to reduce as many uncertainties about the devices as possible. This can be done by making sure every citizen has the channels to ask questions, give feedback and even raise their voices against these devices anonymously. It is also part of the municipalities “inclusive digital city” ambitions and interests to work on such platforms. In conclusion, the application is one such channel that the city should be developing for informing and empowering their citizens and the recommendations given in the report can be the first steps for fully developing the application.

The role that smart technologies play and the data they generate is becoming central in cities; cities are adopting technologies as a new way of governance and making policies based on data. Increasingly, governments and companies implement data-driven strategies, and the number of sensors in the public space is increasing. This translates into abysmal amounts of data about people, their behavior and habits. While the use of smart technologies in cities and specifically in Amsterdam has many potential benefits—from improving local economy and transportation to quality of life and e-governance—there is a growing concern about the ethical risks. Martinez-Balleste et al. (2013) identify that their use in cities jeopardize citizens’ privacy. Moreover, one of the main problems of smart technologies in the public realm is that they are often placed without users’ consent and without providing any notice of their presence. At the same time, the attitude of smart city residents towards this new paradigm — along with the ethical, social and privacy implications that derive from it — is one of indifference. The misalignment between smart city residents attitudes and privacy behaviour is what motivates this project. This project aims to engage smart city residents with smart technologies in order to create awareness, better understanding of the privacy implications and build a democratic process for smart cities. The design solution focuses on empowering smart city users by making smart technologies, its privacy implications and data insights legible for users. By building this knowledge and making privacy implications more visible, users will be able to notice smart technologies in the public realm and spread awareness. Moreover, they will be better equipped to make inform decisions about smart city projects and co-shape the city they envision.

This report is the result of a master thesis project that finalizes 2 years of Strategic Product Design master program at the Industrial Design Engineering faculty. The project is about designing a data-informed clinical care process for Transcatheter Aortic Valve Implantation (TAVI) patient. Transcatheter Aortic Valve Implantation (TAVI) is a treatment that aims to reduce symptoms of Aortic Valve Stenosis (AOS). Most TAVI patients are in the above-75 population. The project brief is to improve TAVI patient's experience using sensor data.
To understand the context of TAVI care pathway, a literature review and stakeholders interviews were done, resulting in a comprehensive understanding of the care pathway. The opportunity to improve the period after TAVI was addressed. To understand the underlying problem among patients in the post-TAVI period, text analysis from the literature was done, followed by coding, categorizing and framework creation. The framework was iterated in a series of 5 co-reflection sessions with the stakeholders. The result is that TAVI patients are managing confrontations throughout the care pathway, which prevent/ limit them from doing enough physical activity. The proposed vision is a continuous and collaborative goal setting system using sensor data. A concept ideation was done, followed by concept testing sessions that resulted in the list of concept requirements. The final proposed product-service system concept is called LIVEsense, a continuous and collaborative goal setting system using sensor data and voice-based conversational agent. LIVEsense consists of three main steps: baseline collection, collaborative goal setting and monitored goal pursuit. Patient wears the sensor and interacting with the voice assistant during the monitored goal pursuit. The sensor collects and compares patient's heart rate and activity data. The proposed product-service system was validated in a co-reflection session with stakeholders. LIVEsense satisfied all elements of the Quadruple Aim (Bodenheimer & Sinksky, 2014) and it brings most value for the period right after TAVI. Other elements that needs to be further explored are the implementation of LIVEsense in the care pathway that consists of multiple organizations.