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P. Kun
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11 records found
1
Conference paper
(2023)
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Willem Van Der Maden, Evert Van Beek, Iohanna Nicenboim, Vera Van Der Burg, Peter Kun, James Derek Lomas, Eunsu Kang
This one day workshop will explore the use of Generative Artificial Intelligence (GenAI) in design research and practice. Generative technologies are developing rapidly and many designers are using them. Yet, there remains little published work on the use of GenAI in design. Our goal is to not only showcase the potential of GenAI for design, but to engage in discussions of its shortcomings and opportunities as they have been already articulated by scholars. By synthesizing both published and unpublished works, we will develop best practices, ethical considerations, and future research directions for the use of GenAI in design. We will explore a range of topics and themes, including leveraging the characteristics of GenAI for design, mapping the diverse applications of GenAI in design, envisioning a framework for design, and guiding future work on GenAI in design research. Ultimately, we hope to provide a roadmap for the integration of GenAI into the design research process and to encourage designers and researchers to explore the potential of GenAI in a thoughtful and deliberate way.
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This one day workshop will explore the use of Generative Artificial Intelligence (GenAI) in design research and practice. Generative technologies are developing rapidly and many designers are using them. Yet, there remains little published work on the use of GenAI in design. Our goal is to not only showcase the potential of GenAI for design, but to engage in discussions of its shortcomings and opportunities as they have been already articulated by scholars. By synthesizing both published and unpublished works, we will develop best practices, ethical considerations, and future research directions for the use of GenAI in design. We will explore a range of topics and themes, including leveraging the characteristics of GenAI for design, mapping the diverse applications of GenAI in design, envisioning a framework for design, and guiding future work on GenAI in design research. Ultimately, we hope to provide a roadmap for the integration of GenAI into the design research process and to encourage designers and researchers to explore the potential of GenAI in a thoughtful and deliberate way.
The emergence of the internet and subsequent massive data collection and storage is creating vast opportunities for design research and practice. In this dissertation, we investigate the interrelationship between design and data science practices and explore data as a new creative lens for design inquiry. While digital data has been increasingly used by designers, such as using A/B testing to drive design decisions for internet products, data has been less explored as a resource for inquiry about the world. Despite how data-connected artifacts increasingly facilitate human interactions, designers’ repertoire still primarily relies on practices established for inquiring in the physical world. The current industry practice of integrating data scientists into the design team is neither affordable nor feasible to apply across the vast majority of contexts and cases where design operates. To address these problems, in this dissertation, we aim to deepen the theoretical and practical knowledge on the intersection of design and data science, and to develop methodological contributions to support future data-rich design practices. The main research question we pursue in this dissertation is “How can designers integrate data practices into design inquiry?” We address this question through conducting a Research-through-Design program to gain, on the one hand, a better understanding of how the fields of design and data science intersect, and on the other hand, to develop methodological contributions for future data-rich design practices. The resulting conceptual framework of Design Inquiry Through Data has been constructed throughout a series of empirical studies in which data-rich design practices are studied. For each study, practical data methods and techniques have been curated and/or developed...
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The emergence of the internet and subsequent massive data collection and storage is creating vast opportunities for design research and practice. In this dissertation, we investigate the interrelationship between design and data science practices and explore data as a new creative lens for design inquiry. While digital data has been increasingly used by designers, such as using A/B testing to drive design decisions for internet products, data has been less explored as a resource for inquiry about the world. Despite how data-connected artifacts increasingly facilitate human interactions, designers’ repertoire still primarily relies on practices established for inquiring in the physical world. The current industry practice of integrating data scientists into the design team is neither affordable nor feasible to apply across the vast majority of contexts and cases where design operates. To address these problems, in this dissertation, we aim to deepen the theoretical and practical knowledge on the intersection of design and data science, and to develop methodological contributions to support future data-rich design practices. The main research question we pursue in this dissertation is “How can designers integrate data practices into design inquiry?” We address this question through conducting a Research-through-Design program to gain, on the one hand, a better understanding of how the fields of design and data science intersect, and on the other hand, to develop methodological contributions for future data-rich design practices. The resulting conceptual framework of Design Inquiry Through Data has been constructed throughout a series of empirical studies in which data-rich design practices are studied. For each study, practical data methods and techniques have been curated and/or developed...
The increasing availability of large-scale datasets such as sensor data or social media data and increasingly accessible data science tools create unique
opportunities for design. However, the relationship between data science practices and design methods is still underdeveloped. In this paper, we propose that data exploration activities can be effectively embedded within a broader design inquiry framework and define a new design method, coined Data Exploration for Design, to support methodical designerly data exploration. The design method addresses the novice’s learning curve and supporting developing a data exploration inquiry mindset with procedures and curated tools. The empirical evaluation highlights support for producing exploration outcomes that are worth the additional technical effort. We close the paper by positioning the
findings in design methodology literature and motivating data exploration principles for design inquiry. The principles urge to acknowledge biases in data collection, spending time with the data, using visualizations as a means-to-anend, and designers being part of the data collection ...
opportunities for design. However, the relationship between data science practices and design methods is still underdeveloped. In this paper, we propose that data exploration activities can be effectively embedded within a broader design inquiry framework and define a new design method, coined Data Exploration for Design, to support methodical designerly data exploration. The design method addresses the novice’s learning curve and supporting developing a data exploration inquiry mindset with procedures and curated tools. The empirical evaluation highlights support for producing exploration outcomes that are worth the additional technical effort. We close the paper by positioning the
findings in design methodology literature and motivating data exploration principles for design inquiry. The principles urge to acknowledge biases in data collection, spending time with the data, using visualizations as a means-to-anend, and designers being part of the data collection ...
The increasing availability of large-scale datasets such as sensor data or social media data and increasingly accessible data science tools create unique
opportunities for design. However, the relationship between data science practices and design methods is still underdeveloped. In this paper, we propose that data exploration activities can be effectively embedded within a broader design inquiry framework and define a new design method, coined Data Exploration for Design, to support methodical designerly data exploration. The design method addresses the novice’s learning curve and supporting developing a data exploration inquiry mindset with procedures and curated tools. The empirical evaluation highlights support for producing exploration outcomes that are worth the additional technical effort. We close the paper by positioning the
findings in design methodology literature and motivating data exploration principles for design inquiry. The principles urge to acknowledge biases in data collection, spending time with the data, using visualizations as a means-to-anend, and designers being part of the data collection
opportunities for design. However, the relationship between data science practices and design methods is still underdeveloped. In this paper, we propose that data exploration activities can be effectively embedded within a broader design inquiry framework and define a new design method, coined Data Exploration for Design, to support methodical designerly data exploration. The design method addresses the novice’s learning curve and supporting developing a data exploration inquiry mindset with procedures and curated tools. The empirical evaluation highlights support for producing exploration outcomes that are worth the additional technical effort. We close the paper by positioning the
findings in design methodology literature and motivating data exploration principles for design inquiry. The principles urge to acknowledge biases in data collection, spending time with the data, using visualizations as a means-to-anend, and designers being part of the data collection
The current work investigates how creativity manifests when designers use data work in the early phase of design. Designers are increasingly interested in utilizing the massive amounts of data surrounding our everyday lives. However, data work is still challenging to incorporate into the design process. In this paper, we present a case study with three novice design teams who were tasked to integrate data work into their design process. During the study, we observed how creativity took place in framing a design problem. We present and discuss their actions from a creativity process perspective, highlighting how they used and rationalized data-inspired inquiries creatively in the early phase of design. The current results inform the development of a design framework to structure data work methodologically and coherently into design processes. We coin this design framework Exploratory Data Inquiry.
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The current work investigates how creativity manifests when designers use data work in the early phase of design. Designers are increasingly interested in utilizing the massive amounts of data surrounding our everyday lives. However, data work is still challenging to incorporate into the design process. In this paper, we present a case study with three novice design teams who were tasked to integrate data work into their design process. During the study, we observed how creativity took place in framing a design problem. We present and discuss their actions from a creativity process perspective, highlighting how they used and rationalized data-inspired inquiries creatively in the early phase of design. The current results inform the development of a design framework to structure data work methodologically and coherently into design processes. We coin this design framework Exploratory Data Inquiry.
Even though emerging city-makers are increasingly organized to trigger social changes, it is still hard to apprehend their real power to transform space and the way we live together. In this chapter, we explore how designerly approaches, such as hacking, making, and prototyping, can empower emerging city-makers to trigger a broader change and transformation process. It can be concluded that hackable city-making can make a difference when combining top-down public management with bottom-up social innovation. A patchable plug-in platform might enable emerging city-makers to create value for the city and for society. However, it asks for new ways of participatory governance that enable these emerging, heterogeneous city-makers to participate actively in exploring the collaborative envisioned potential and to have constructive dialogues aiming for transformational change for the common good.
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Even though emerging city-makers are increasingly organized to trigger social changes, it is still hard to apprehend their real power to transform space and the way we live together. In this chapter, we explore how designerly approaches, such as hacking, making, and prototyping, can empower emerging city-makers to trigger a broader change and transformation process. It can be concluded that hackable city-making can make a difference when combining top-down public management with bottom-up social innovation. A patchable plug-in platform might enable emerging city-makers to create value for the city and for society. However, it asks for new ways of participatory governance that enable these emerging, heterogeneous city-makers to participate actively in exploring the collaborative envisioned potential and to have constructive dialogues aiming for transformational change for the common good.
Conference paper
(2019)
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Annika Wolff, Antti Knutas, Victoria Palacin, Cathrine Seidelin, Péter Kun, Ingrid Mulder
This workshop is concerned with the potentials and barriers of data in facilitating bottom-up innovation. We focus on two specific communities that could benefit from utilizing data, but may struggle currently. Firstly, civil society participating in grassroots actions, who use public data for innovation and to strengthen their advocacy. Secondly, organisations - especially SME's - who want to make use of data, especially large complex data sets, to design data-driven services.
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This workshop is concerned with the potentials and barriers of data in facilitating bottom-up innovation. We focus on two specific communities that could benefit from utilizing data, but may struggle currently. Firstly, civil society participating in grassroots actions, who use public data for innovation and to strengthen their advocacy. Secondly, organisations - especially SME's - who want to make use of data, especially large complex data sets, to design data-driven services.
The current work elaborates upon a Generative Data Exploration method, which is a design technique aiming at supporting designers in integrating data in their design activities. Digital data offers new opportunities in all sort of professional domains, yet existing approaches and tools to manipulate data are predominantly targeted at data experts. As access to data is becoming democratised, new types of techniques are needed to leverage the agency of designers and to empower them to utilise data in the design process. Designers without prior data experience can benefit from the techniques, know-how, best practices of experts, if such expert knowledge is codified in design methods and tools. The aims of a Generative Data Exploration method are two-fold. First, the method facilitates a learning curve on gaining holistic data literacy.
Second, the method supports designing where digital data, exploration of data and sense-making of data is part of the process.
...
The current work elaborates upon a Generative Data Exploration method, which is a design technique aiming at supporting designers in integrating data in their design activities. Digital data offers new opportunities in all sort of professional domains, yet existing approaches and tools to manipulate data are predominantly targeted at data experts. As access to data is becoming democratised, new types of techniques are needed to leverage the agency of designers and to empower them to utilise data in the design process. Designers without prior data experience can benefit from the techniques, know-how, best practices of experts, if such expert knowledge is codified in design methods and tools. The aims of a Generative Data Exploration method are two-fold. First, the method facilitates a learning curve on gaining holistic data literacy.
Second, the method supports designing where digital data, exploration of data and sense-making of data is part of the process.
Design Enquiry Through Data
Appropriating a Data Science Workflow for the Design Process
The recent developments in data science and end-user data tools indicate an opportunity for designers to adapt new data tools for design enquiry. Data has an unquestionable role in the future of the design practice for creating new digital products and services. Today’s data deluge also opens up new ways of enquiring about the world through data. The current work explores how designers could appropriate a data science workflow in their design research process. Two studies are conducted to explore how a data science workflow could be adapted into a design research process. We present how the participants appropriated data techniques for creative uses and how they synthesized a data-centric enquiry into their research process. We found that designers appropriate data using their creative capacities in hypothesis forming for data collection and exploratory data analysis, and we highlight some implications of this. Our findings can inform the design space of the creativity support of future data tools and future data-centric design methods.
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The recent developments in data science and end-user data tools indicate an opportunity for designers to adapt new data tools for design enquiry. Data has an unquestionable role in the future of the design practice for creating new digital products and services. Today’s data deluge also opens up new ways of enquiring about the world through data. The current work explores how designers could appropriate a data science workflow in their design research process. Two studies are conducted to explore how a data science workflow could be adapted into a design research process. We present how the participants appropriated data techniques for creative uses and how they synthesized a data-centric enquiry into their research process. We found that designers appropriate data using their creative capacities in hypothesis forming for data collection and exploratory data analysis, and we highlight some implications of this. Our findings can inform the design space of the creativity support of future data tools and future data-centric design methods.
Digital data has a considerable role in our everyday lives: we use publicly available data to find out about weather, traffic or pollution, we track ourselves and we release our private data to monitor our health and to get advices from our favourite apps, we relate on services that digest big amount of data to predict what will happen next. In this era of “living services”, what kind of data literacy is needed to equip a service designer? Is there a need to rethink of service design tools so that data will be explicitly taken into account in the design process? Is there a need to update service design curricula to embrace these challenges? All of these questions will be discussed through a specific case: a workshop on data exploration held at the Service Systems Design Master at Aalborg University in Copenhagen to investigate the role of data literacy in a service design university program.
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Digital data has a considerable role in our everyday lives: we use publicly available data to find out about weather, traffic or pollution, we track ourselves and we release our private data to monitor our health and to get advices from our favourite apps, we relate on services that digest big amount of data to predict what will happen next. In this era of “living services”, what kind of data literacy is needed to equip a service designer? Is there a need to rethink of service design tools so that data will be explicitly taken into account in the design process? Is there a need to update service design curricula to embrace these challenges? All of these questions will be discussed through a specific case: a workshop on data exploration held at the Service Systems Design Master at Aalborg University in Copenhagen to investigate the role of data literacy in a service design university program.
OpenDataLabs
New Infrastructures for Open Data Commons
Poster
(2017)
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G. Concilio, F Molinari, M. Aguilar, T. Edman, A.S. Sorensen, N. Morelli, L.K. Torntoft, A. De Götzen, Ingrid Mulder, Tomasz Jaskiewicz, Péter Kun, J. Pedersen, P. Ammentorp
Prototyping for Citizen Engagement
Workshop outcomes Design and the City Conference, 22 April 2016
This document aims to bring together snippets from the Prototyping Citizen Engagement workshop that happened at the Design and the City conference in Amsterdam, on 22 April 2016.
We wish to have this document be a summary of the workshop, consisting of snapshots of the discussions that went into several directions along citizen engagement, the role of the government, how to scale up interventions, how to foster systemic change and so forth.
The about 20 people present at the workshop offered diverse perspectives on the agenda. The debates along certain topics were sometimes heated or controversial, but “moderate provocation” did trigger further depth in reflection.
This document is not aimed at being conclusive, but to be a goto
reference to recap what happened during the workshop. Last, but not least, we would like to thank again the participants who had been at the workshop and played along with us. We learned a lot, and we hope that you did too. ...
We wish to have this document be a summary of the workshop, consisting of snapshots of the discussions that went into several directions along citizen engagement, the role of the government, how to scale up interventions, how to foster systemic change and so forth.
The about 20 people present at the workshop offered diverse perspectives on the agenda. The debates along certain topics were sometimes heated or controversial, but “moderate provocation” did trigger further depth in reflection.
This document is not aimed at being conclusive, but to be a goto
reference to recap what happened during the workshop. Last, but not least, we would like to thank again the participants who had been at the workshop and played along with us. We learned a lot, and we hope that you did too. ...
This document aims to bring together snippets from the Prototyping Citizen Engagement workshop that happened at the Design and the City conference in Amsterdam, on 22 April 2016.
We wish to have this document be a summary of the workshop, consisting of snapshots of the discussions that went into several directions along citizen engagement, the role of the government, how to scale up interventions, how to foster systemic change and so forth.
The about 20 people present at the workshop offered diverse perspectives on the agenda. The debates along certain topics were sometimes heated or controversial, but “moderate provocation” did trigger further depth in reflection.
This document is not aimed at being conclusive, but to be a goto
reference to recap what happened during the workshop. Last, but not least, we would like to thank again the participants who had been at the workshop and played along with us. We learned a lot, and we hope that you did too.
We wish to have this document be a summary of the workshop, consisting of snapshots of the discussions that went into several directions along citizen engagement, the role of the government, how to scale up interventions, how to foster systemic change and so forth.
The about 20 people present at the workshop offered diverse perspectives on the agenda. The debates along certain topics were sometimes heated or controversial, but “moderate provocation” did trigger further depth in reflection.
This document is not aimed at being conclusive, but to be a goto
reference to recap what happened during the workshop. Last, but not least, we would like to thank again the participants who had been at the workshop and played along with us. We learned a lot, and we hope that you did too.