A. Gomez Ortega
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1
Sensitive Data Donation
Reframing Intimate Data Practices through Design
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.
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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.
Sensitive Data Donation
A Feminist Reframing of Data Practices for Intimate Research Contexts
Data donation is an emerging practice for collecting personal data. However, recent data donation approaches are insufficient in intimate research contexts as they perceive data as neutral and objective and do not consider the contexts where data is generated and shaped nor offer choices beyond whether to disclose data. In this paper, we investigate how Data Feminism can inform an alternative form of data donation and propose the Sensitive Data Donation (sDD) method. It recognizes the sensitive nature of data and assumes the importance of situating and contextualizing it through balanced participation from donors, either as contributors, collaborators, or co-creators. To develop the method, we conduct a scoping literature review where we conceptualize data donation theories and practices. These serve as a base to critique recent approaches and propose an alternative: sDD. It comprises five principles integrated into a five-phase approach. We conclude by discussing its limitations and future challenges.
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Data donation is an emerging practice for collecting personal data. However, recent data donation approaches are insufficient in intimate research contexts as they perceive data as neutral and objective and do not consider the contexts where data is generated and shaped nor offer choices beyond whether to disclose data. In this paper, we investigate how Data Feminism can inform an alternative form of data donation and propose the Sensitive Data Donation (sDD) method. It recognizes the sensitive nature of data and assumes the importance of situating and contextualizing it through balanced participation from donors, either as contributors, collaborators, or co-creators. To develop the method, we conduct a scoping literature review where we conceptualize data donation theories and practices. These serve as a base to critique recent approaches and propose an alternative: sDD. It comprises five principles integrated into a five-phase approach. We conclude by discussing its limitations and future challenges.
Participation in Data Donation
Co-Creative, Collaborative, and Contributory Engagements with Athletes and Their Intimate Data
Data donation is an emerging practice enabling personal data collection for research. While it offers opportunities to access new insights into people’s behavior and experiences through their digital-trace data, the role of individuals – as research participants – is limited in most data donation projects. They primarily contribute with data, limiting the perspectives included and accounted for around critical research-design decisions. In this paper, we explore the opportunity to embed data donation in research processes that are not only contributory but collaborative and co-created. To do so, we present a participatory data donation case study focused on athletes’ perceptions of the impact of their menstrual cycle on their sports performance through their physical activity data. Based on the data donation experiences of 20 athletes, our paper provides insights into people’s preferences and expectations in participatory data donation processes and discusses considerations for supporting various degrees of participation in future data donation research.
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Data donation is an emerging practice enabling personal data collection for research. While it offers opportunities to access new insights into people’s behavior and experiences through their digital-trace data, the role of individuals – as research participants – is limited in most data donation projects. They primarily contribute with data, limiting the perspectives included and accounted for around critical research-design decisions. In this paper, we explore the opportunity to embed data donation in research processes that are not only contributory but collaborative and co-created. To do so, we present a participatory data donation case study focused on athletes’ perceptions of the impact of their menstrual cycle on their sports performance through their physical activity data. Based on the data donation experiences of 20 athletes, our paper provides insights into people’s preferences and expectations in participatory data donation processes and discusses considerations for supporting various degrees of participation in future data donation research.
Personal Data Comics
A Data Storytelling Approach Supporting Personal Data Literacy
Most people interact with digital technologies that collect personal data about their behavior and experiences, leaving behind a data trail. The data within this trail is abstract and difficult to interpret; still, people often need to decide about its collection and distribution. Hence, it is paramount to support personal data literacy, for which data visualization approaches have been successful. These approaches focus mostly on data from single sources (e.g., IoT devices at home) or types (e.g., menstrual logs) and fail to capture people’s situated knowledge. We hypothesize that creating data comics can address these limitations and support people in developing personal data literacy. In this paper, we explore how non-data experts create personal data comics, starting from simple data visualizations, and investigate their effectiveness and engagement in the context of pregnancy. Doing so, we identify comic elements that facilitate the autonomous exploration of personal data and provide design recommendations to support independent data comic creation.
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Most people interact with digital technologies that collect personal data about their behavior and experiences, leaving behind a data trail. The data within this trail is abstract and difficult to interpret; still, people often need to decide about its collection and distribution. Hence, it is paramount to support personal data literacy, for which data visualization approaches have been successful. These approaches focus mostly on data from single sources (e.g., IoT devices at home) or types (e.g., menstrual logs) and fail to capture people’s situated knowledge. We hypothesize that creating data comics can address these limitations and support people in developing personal data literacy. In this paper, we explore how non-data experts create personal data comics, starting from simple data visualizations, and investigate their effectiveness and engagement in the context of pregnancy. Doing so, we identify comic elements that facilitate the autonomous exploration of personal data and provide design recommendations to support independent data comic creation.
Most people are entangled with an ever-growing trail of data that results from their daily interactions with products and services. Yet, they are hardly aware of the nature and characteristics of the data within this trail. We design dataslip, a provocative artifact that materializes the personal data trail into a receipt and aims to elicit creepiness. We demonstrate dataslip at two events in Delft, The Netherlands. Dataslip is a starting point to foster conversations with local community members about the underlying challenges and potential alternatives to personal data collection and use. We use these as prompts for further speculation through a collaborative futuring exercise with children, where we part from challenges towards hopeful and empowering futures. We contribute with an artifact that invites individuals to interrogate the current personal data practices they are embedded in and a set of five speculative design scenarios that suggest hopeful and empowering alternatives.
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Most people are entangled with an ever-growing trail of data that results from their daily interactions with products and services. Yet, they are hardly aware of the nature and characteristics of the data within this trail. We design dataslip, a provocative artifact that materializes the personal data trail into a receipt and aims to elicit creepiness. We demonstrate dataslip at two events in Delft, The Netherlands. Dataslip is a starting point to foster conversations with local community members about the underlying challenges and potential alternatives to personal data collection and use. We use these as prompts for further speculation through a collaborative futuring exercise with children, where we part from challenges towards hopeful and empowering futures. We contribute with an artifact that invites individuals to interrogate the current personal data practices they are embedded in and a set of five speculative design scenarios that suggest hopeful and empowering alternatives.
Beyond data transactions
A framework for meaningfully informed data donation
Journal article
(2023)
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Alejandra Gomez Ortega, Jacky Bourgeois, Wiebke Toussaint Hutiri, Gerd Kortuem
As we navigate physical (e.g., supermarket) and digital (e.g., social media) systems, we generate personal data about our behavior. Researchers and designers increasingly rely on this data and appeal to several approaches to collect it. One of these is data donation, which encourages people to voluntarily transfer their (personal) data collected by external parties to a specific cause. One of the central pillars of data donation is informed consent, meaning people should be adequately informed about what and how their data will be used. However, can we be adequately informed when it comes to donating our data when many times we don’t even know it is being collected and, even more so, what exactly is being collected? In this paper, we investigate how to foster (personal) data literacy and increase donors’ understanding of their data. We introduce a Research through Design approach where we define a data donation journey in the context of speech records, data collected by Google Assistant. Based on the data donation experiences of 22 donors, we propose a data donation framework that understands and approaches data donation as an encompassing process with mutual benefit for donors and researchers. Our framework supports a donation process that dynamically and iteratively engages donors in exploring and understanding their data and invites them to (re)evaluate and (re)assess their participation. Through this process, donors increase their data literacy and are empowered to give meaningfully informed consent.
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As we navigate physical (e.g., supermarket) and digital (e.g., social media) systems, we generate personal data about our behavior. Researchers and designers increasingly rely on this data and appeal to several approaches to collect it. One of these is data donation, which encourages people to voluntarily transfer their (personal) data collected by external parties to a specific cause. One of the central pillars of data donation is informed consent, meaning people should be adequately informed about what and how their data will be used. However, can we be adequately informed when it comes to donating our data when many times we don’t even know it is being collected and, even more so, what exactly is being collected? In this paper, we investigate how to foster (personal) data literacy and increase donors’ understanding of their data. We introduce a Research through Design approach where we define a data donation journey in the context of speech records, data collected by Google Assistant. Based on the data donation experiences of 22 donors, we propose a data donation framework that understands and approaches data donation as an encompassing process with mutual benefit for donors and researchers. Our framework supports a donation process that dynamically and iteratively engages donors in exploring and understanding their data and invites them to (re)evaluate and (re)assess their participation. Through this process, donors increase their data literacy and are empowered to give meaningfully informed consent.
Conference paper
(2023)
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A. Gomez Ortega, Peter Lovei, Renee Noortman, Romain Toebosch, Alex Bowyer, Albrecht Kurze, Mathias Funk, Sandy Gould, Samuel Huron, J. Bourgeois
Behavioral data is ubiquitous in products, services, and systems that people interact with. It is increasingly used by design and HCI researchers and practitioners throughout their human-centered and participatory design processes. The highly dynamic nature of behavioral data makes it deeply intertwined with people, their behavior, and their experiences. Thus, it presents unique opportunities and challenges. This Special Interest Group will provide a space to reflect and discuss effective and responsible ways to engage with behavioral data in human-centered design processes. We will explore questions about the types and scale of data used, the contexts in which data is embedded and applied, the methods we rely on, and the forms of engagement of the multiple stakeholders. In doing so, our goal is to collaboratively develop a research agenda, setting the scope for an annual, international symposium on Data-Centric Design.
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Behavioral data is ubiquitous in products, services, and systems that people interact with. It is increasingly used by design and HCI researchers and practitioners throughout their human-centered and participatory design processes. The highly dynamic nature of behavioral data makes it deeply intertwined with people, their behavior, and their experiences. Thus, it presents unique opportunities and challenges. This Special Interest Group will provide a space to reflect and discuss effective and responsible ways to engage with behavioral data in human-centered design processes. We will explore questions about the types and scale of data used, the contexts in which data is embedded and applied, the methods we rely on, and the forms of engagement of the multiple stakeholders. In doing so, our goal is to collaboratively develop a research agenda, setting the scope for an annual, international symposium on Data-Centric Design.
Conference paper
(2023)
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Alejandra Gómez Ortega, Vasileios Milias, James Scott Broadhead, Carlo van der Valk, Jacky Bourgeois
As we navigate the physical and digital world, we unknowingly leave behind an immense trail of data. We are informed about this via lengthy documents (e.g., privacy policies) or short statements (e.g., cookie popups). However, even when we know that data is collected, we remain largely unaware of its nature; what information it contains and how it relates to us. Data is highly personal. It contains and reveals information about our behavior and experiences scattered over time, which can be abstract and opaque even to us. Dataslip is an interactive installation where the construct of personal data is translated into a material and tangible representation in the form of a receipt or ‘personal data slip’. The receipt contains detailed information and illustrative examples of the data generated from our interactions with five different categories of products and services: (1) personalized public transport cards, (2) supermarket loyalty cards, (3) credit and debit cards, (4) wearables, and (5) mobile apps. Its length is proportional to the amount of data collected about us. With dataslip, we aim to reduce the distance between individuals and their personal data, elicit confrontation and invite people to question their role within the personal data ecosystems in which they are embedded.
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As we navigate the physical and digital world, we unknowingly leave behind an immense trail of data. We are informed about this via lengthy documents (e.g., privacy policies) or short statements (e.g., cookie popups). However, even when we know that data is collected, we remain largely unaware of its nature; what information it contains and how it relates to us. Data is highly personal. It contains and reveals information about our behavior and experiences scattered over time, which can be abstract and opaque even to us. Dataslip is an interactive installation where the construct of personal data is translated into a material and tangible representation in the form of a receipt or ‘personal data slip’. The receipt contains detailed information and illustrative examples of the data generated from our interactions with five different categories of products and services: (1) personalized public transport cards, (2) supermarket loyalty cards, (3) credit and debit cards, (4) wearables, and (5) mobile apps. Its length is proportional to the amount of data collected about us. With dataslip, we aim to reduce the distance between individuals and their personal data, elicit confrontation and invite people to question their role within the personal data ecosystems in which they are embedded.
Digital technologies have increasingly integrated into people's lives, continuously capturing their behavior through potentially sensitive data. In the context of voice assistants, there is a misalignment between experts, regulators, and users on whether and what data is 'sensitive', partly due to how data is presented to users; as single interactions. We investigate users' perspectives on the sensitivity and intimacy of their Google Assistant speech records, introduced comprehensively as single interactions, patterns, and inferences. We collect speech records through data donation and explore them in collaboration with 17 users during interviews based on predefined data-sharing scenarios. Our results indicate a tipping point in perceived sensitivity and intimacy as participants delve deeper into their data and the information derived from it. We propose a conceptualization of sensitivity and intimacy that accounts for the fuzzy nature of data and must disentangle from it. We discuss the implications of our findings and provide recommendations.
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Digital technologies have increasingly integrated into people's lives, continuously capturing their behavior through potentially sensitive data. In the context of voice assistants, there is a misalignment between experts, regulators, and users on whether and what data is 'sensitive', partly due to how data is presented to users; as single interactions. We investigate users' perspectives on the sensitivity and intimacy of their Google Assistant speech records, introduced comprehensively as single interactions, patterns, and inferences. We collect speech records through data donation and explore them in collaboration with 17 users during interviews based on predefined data-sharing scenarios. Our results indicate a tipping point in perceived sensitivity and intimacy as participants delve deeper into their data and the information derived from it. We propose a conceptualization of sensitivity and intimacy that accounts for the fuzzy nature of data and must disentangle from it. We discuss the implications of our findings and provide recommendations.
Recently, methods and approaches such as Participatory Data Analysis, Data-Enabled Design, and Contextual Inquiry have highlighted how design activities can benefit from behavioral data. This data offers new ways to learn from what people do and how they do it, across time and space. However, behavioral data introduces changes and frictions to design activities and poses several challenges for designers to overcome. In this paper, we conduct two workshops with 18 expert designers, from industry and academia, to understand the nature of these challenges, beyond the technical aspects. We contribute by underlining the challenges and opportunities of incorporating behavioral data into design activities; including a design perspective on data, interacting with participants, and interacting with regulatory bodies. We translate our findings into opportunities for a better alignment between regulatory bodies, designers, and participants. We propose to harness the iterative nature of design activities and embedded it into a process that allows for continuous reflection, reassessment, and review of highly dynamic datasets.
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Recently, methods and approaches such as Participatory Data Analysis, Data-Enabled Design, and Contextual Inquiry have highlighted how design activities can benefit from behavioral data. This data offers new ways to learn from what people do and how they do it, across time and space. However, behavioral data introduces changes and frictions to design activities and poses several challenges for designers to overcome. In this paper, we conduct two workshops with 18 expert designers, from industry and academia, to understand the nature of these challenges, beyond the technical aspects. We contribute by underlining the challenges and opportunities of incorporating behavioral data into design activities; including a design perspective on data, interacting with participants, and interacting with regulatory bodies. We translate our findings into opportunities for a better alignment between regulatory bodies, designers, and participants. We propose to harness the iterative nature of design activities and embedded it into a process that allows for continuous reflection, reassessment, and review of highly dynamic datasets.
Conference paper
(2022)
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A. Gomez Ortega, Janne van Kollenburg, Yvette Shen, D.S. Murray-Rust, Dajana Nedić, Juan Jimenez Garcia, Wo Meijer, Pranshu Kumar Chaudhary, J. Bourgeois
Designers and HCI researchers from industry and academia have been exploring the opportunities that emerge from incorporating behavioral data into the design process. For this, designers employ and combine data from multiple sources, multiple scales, and types to obtain valuable insights that inform and support design decisions. This combination unfolds through interdisciplinary collaborations, enabled by various methods and approaches, including participatory data analysis, sense-making interviews, co-design workshops, and data storytelling. However, due to the personal nature of behavioral data and the open-ended, iterative approach of HumanCentered Design, data-centric design activities clash with current HCI and data science practices. As both industry and academia increasingly use data-centric design processes, we recognize a need to share both examples and experiences to reinforce that most practices (and failed experiences) do not yet emerge solely from the literature. In this Special Interest Group, we aim to provide a space for design, data, and HCI researchers and practitioners to connect, reflect on the current practices, and explore potential approaches to further integrating behavioral data into design activities.
...
Designers and HCI researchers from industry and academia have been exploring the opportunities that emerge from incorporating behavioral data into the design process. For this, designers employ and combine data from multiple sources, multiple scales, and types to obtain valuable insights that inform and support design decisions. This combination unfolds through interdisciplinary collaborations, enabled by various methods and approaches, including participatory data analysis, sense-making interviews, co-design workshops, and data storytelling. However, due to the personal nature of behavioral data and the open-ended, iterative approach of HumanCentered Design, data-centric design activities clash with current HCI and data science practices. As both industry and academia increasingly use data-centric design processes, we recognize a need to share both examples and experiences to reinforce that most practices (and failed experiences) do not yet emerge solely from the literature. In this Special Interest Group, we aim to provide a space for design, data, and HCI researchers and practitioners to connect, reflect on the current practices, and explore potential approaches to further integrating behavioral data into design activities.
Reconstructing Intimate Contexts through Data Donation
A Case Study in Menstrual Tracking Technologies
Ubiquitous wearable and mobile technologies generate vast amounts of data from sensors and self-logging applications. This data creates opportunities to better understand people’s behavior and inform research on intimate topics such as menstruation. However, in design and HCI research, reconstructing the context in which data was collected and understanding the lived experience behind the data often requires the active participation of people. In this paper, we augment the concept of data donation beyond data collection to explore the possibilities of actively engaging data donors in the (intimate) interpretation of their data. Specifically, we define and implement a menstrual logs data donation journey. We received data sets from 35 donors over five weeks, 13 of whom participated in reconstructing the context of their data. We translate our experience into a conceptualization of designerly data donation around the data, data donors, and data receivers, which we discuss along with its implications.
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Ubiquitous wearable and mobile technologies generate vast amounts of data from sensors and self-logging applications. This data creates opportunities to better understand people’s behavior and inform research on intimate topics such as menstruation. However, in design and HCI research, reconstructing the context in which data was collected and understanding the lived experience behind the data often requires the active participation of people. In this paper, we augment the concept of data donation beyond data collection to explore the possibilities of actively engaging data donors in the (intimate) interpretation of their data. Specifically, we define and implement a menstrual logs data donation journey. We received data sets from 35 donors over five weeks, 13 of whom participated in reconstructing the context of their data. We translate our experience into a conceptualization of designerly data donation around the data, data donors, and data receivers, which we discuss along with its implications.
With the advance of the Internet and the Internet of Things, an abundance of 'big' data becomes available. Data science can be incorporated in design, which brings forward various opportunities for designers to benefit from this new material. However, the designer's perspective and their role remains unclear. How do they think about and approach data? What do they want to achieve with this data? What do they need to take ownership of designing with data? In this paper we take a design perspective to map the opportunities and challenges of leveraging large data-sets as part of the design process. We rely on a survey with 75 participants across a Faculty of Industrial Design Engineering and in-depth reflective interviews with a subset of 9 participants. We discuss the impact of data on the roles designers can adopt as well as an approach to designing with data. This work aims to inform on educational support, data literacy and tools needed for designers to take advantage of this new era of design digitalisation.
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With the advance of the Internet and the Internet of Things, an abundance of 'big' data becomes available. Data science can be incorporated in design, which brings forward various opportunities for designers to benefit from this new material. However, the designer's perspective and their role remains unclear. How do they think about and approach data? What do they want to achieve with this data? What do they need to take ownership of designing with data? In this paper we take a design perspective to map the opportunities and challenges of leveraging large data-sets as part of the design process. We rely on a survey with 75 participants across a Faculty of Industrial Design Engineering and in-depth reflective interviews with a subset of 9 participants. We discuss the impact of data on the roles designers can adopt as well as an approach to designing with data. This work aims to inform on educational support, data literacy and tools needed for designers to take advantage of this new era of design digitalisation.
In-The-wild research allows the HCI community to gain insights into personal behaviour and characteristics. For designers and researchers, this means having access to rich spatiotemporal insights reflecting user's characteristics, behaviours, and needs. However, designerly contexts require contextualized and meaningful data, and collecting it in-The-wild involves a great effort. In addition, ethical implications need to be considered. In this paper, we propose designerly data donation, a participatory approach for data collection in-The-wild, as an effective and ethical way to enable data-centric design processes. We present the potential benefits of designerly data donation around three axes: value gain, data contextualization, and roles and relationships. And we introduce the challenges of designerly data donation at the intersection of HCI, UbiComp, and design.
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In-The-wild research allows the HCI community to gain insights into personal behaviour and characteristics. For designers and researchers, this means having access to rich spatiotemporal insights reflecting user's characteristics, behaviours, and needs. However, designerly contexts require contextualized and meaningful data, and collecting it in-The-wild involves a great effort. In addition, ethical implications need to be considered. In this paper, we propose designerly data donation, a participatory approach for data collection in-The-wild, as an effective and ethical way to enable data-centric design processes. We present the potential benefits of designerly data donation around three axes: value gain, data contextualization, and roles and relationships. And we introduce the challenges of designerly data donation at the intersection of HCI, UbiComp, and design.