Alejandra Gomez Ortega Peter Lovei Renee Noortman Romain Toebosch Alex Bowyer Albrecht Kurze Mathias Funk Sandy J.J. Gould Samuel Huron Jacky Bourgeois

This is an HTML author version of the paper. The published version is available open-access. Please cite the work as:

Gomez Ortega, A; Lovei, P; Noortmann, R; Toebosch, R; Bowyer, A; Kurze, A; Funk, M; Gould, SJJ; Huron, S; Bourgeois, J. (2023). SIG on Data as Human-Centered Design Material. CHI ’23, DOI:10.1145/3544549.3583180

Introduction

Behavioral data is intrinsic to the digital world we live in. It is generated, collected and stored as we navigate physical (e.g., public transport) and digital (e.g., social media) spaces. Hence, it offers a unique perspective of our behavior and experience grounded across time and space. For example, looking at her activity tracker, a data participant could say “in 2020 I was often at home, trying to exercise and keep my daily steps constant; now I move more, but every day is different, and my daily steps vary a lot.” For this reason, design and HCI researchers increasingly integrate behavioral data into their human-centered and participatory design processes (e.g., (Bogers et al. 2016; Gorkovenko et al. 2019; Tolmie et al. 2016; Bourgeois et al. 2014)).

Behavioral data is highly personal. Hence, being part of these participatory processes invites people to engage with their data, which reflects their behavior and serves as a tool for self-reflection. For example, looking at her activity tracker, the same participant could say “my daily steps were below my goal for two days when I was sick”. In this context, design and HCI researchers support people’s engagement with their behavioral data; helping them (1) navigate existing data protection regulations (Bowyer et al. 2022; Gómez Ortega, Bourgeois, and Kortuem 2022), (2) categorize and visualize the data and, in doing so, understand it and its implications (Kurze et al. 2020; Pins et al. 2021), and (3) interpret and situate the data (Kurze et al. 2020; Tolmie et al. 2016; Kollenburg and Bogers 2019; Gómez Ortega, Bourgeois, and Kortuem 2022).

In practice, engaging with behavioral data poses several practical challenges for design and HCI researchers, some of these are well-documented in the literature (Gorkovenko et al. 2020; Lu et al. 2021). For instance, (1) being compliant with regulatory processes that push for anonymization, (2) collecting, manipulating, and shaping the data in a way that fits and supports people’s needs and abilities, and (3) making sense of the data and combining (sometimes contrasting) information from multiple sources and in multiple formats.

As part of the emergent Data-Centric Design community, we believe that having an exchange on ways of working with behavioral data and the challenges we face in doing so, would help to define the foundations of future data-centric design practices, methods, and approaches. This community evolved from a workshop on ‘Designing with Behavioral Data’ held (online and in-person) during the Dutch Design Week 2021¹ in October, 2021.

SIG Goal

CHI’22 was the first milestone of this Special Interest Group (Gomez Ortega et al. 2022), where we mapped the emerging community and established three aims:

1. Identify Best Practices – How and where in the design process is behavioral data collected, applied, and validated? What is a designerly take on collecting and using behavioral data throughout a process that is dynamic and iterative?

2. Co-develop Appropriate Tools – How to foster collaboration between designers and data scientists to create accessible, designer-friendly tools that enable a creative and holistic engagement with data? What are the existing tools and methods that support designers in using data as creative design material?

3. Educate Stakeholders – How to establish a common ground on responsibly setting up and running designerly, data-intensive projects with regulatory bodies (i.e., HREC, IRB)? How to reduce the frictions that emerge from an exploratory, rather than an evaluative, approach to data-intensive activities?

The main outcome of the CHI’22 SIG was a map, collaboratively developed by all participants and remaining open as a living document. It provides an overview of the state of the art in our field, illustrating the type of activities and perspectives through which we engage with behavioral data. In addition, the CHI ’22 SIG connected us, growing through a Slack online space² , international events, including introductory courses at CHI ’22 and NordiCHI ’22 (Lovei, Noortman, and Funk 2022; Lovei et al. 2022), and monthly conversations, where community members introduce themselves and their ways of working with behavioral data³. These activities helped us iteratively draft three core focus areas:

• Data Creation and Access. The ways data is captured shape the conversations it supports. How do we collect or generate behavioral data? Access to behavioral data often relies on the design and development of probes and prototypes (e.g., (Kollenburg and Bogers 2019; Bourgeois et al. 2014; Tolmie et al. 2016)). How do prototypes shape the data? Can we rely on alternative ways to access behavioral data (e.g., crowdsourcing (Pins et al. 2021), data donation (Gómez Ortega, Bourgeois, and Kortuem 2022))? How do these approaches fit and challenge existing data protection regulations and privacy considerations (e.g., the European General Data Protection Regulation (Bowyer et al. 2022))? To what extent do these approaches reinforce or mitigate existing inequalities (D’Ignazio and Klein 2020)?

• Data Mediation and Interaction. The way(s) we represent the data to support interpretation and discussion play a critical role on the insights that emerge. How do we represent and shape behavioral data? Behavioral data is often represented visually through static and dynamic data visualizations (e.g., (Pins et al. 2021; Kurze et al. 2020)) and dashboards (e.g., (Tolmie et al. 2016; Bogers et al. 2016; Berger et al. 2018)). How does the way we represent data influence our design (processes) and our interactions with stakeholders? What factors influence the way we represent data? What tools and techniques do we rely on? What are other ways and means we could use to represent and shape the data (e.g., physical and tangible (Bae et al. 2022; Jofre et al., n.d.; Wun et al. 2019), audible (Young, Marsden, and Coulton 2019))?

• Collaboration around Data. While the lens is ‘data-centric’ – revolving around and supported by behavioral data – the research processes are fundamentally human-centered and participatory (e.g., (Kollenburg and Bogers 2019; Kurze et al. 2020; Clarke et al. 2018)). But who is involved and how? When reporting our experiences with behavioral data, we often fail to shed light on the many hands involved in generating, collecting, storing, processing, analyzing, and visualizing the data (D’Ignazio and Klein 2020). Bringing visibility to those involved throughout a data-centric design process can better inform and support future designers and researchers engaging in similar activities.

CHI is the core venue of our interdisciplinary community. Hence, CHI’23 offers a unique moment for members of our community to align in time if not in space as we favor the hybrid format. With its visibility and credibility, CHI’23 represents a critical opportunity to strengthen and further expand our community beyond our existing structures (e.g., conversations, and courses). During the CHI’23 SIG-CHI event, we aim to collectively and collaboratively develop a Data-Centric Design research agenda, leveraging the three focus areas above as the starting point. This collaboratively defined research agenda will foster meaningful collaborations and feed the organization of an annual, international symposium on Data-Centric Design. Attendees of the SIG-CHI event will form the foundation for a program committee of the symposium.

Description of the Community

The Data-Centric Design community is a growing community of researchers and practitioners at the intersection of Human-Computer Interaction, Participatory Design, and Personal Informatics. As organizers, we have a strong foothold in these three areas. However, we envision engaging with a broader network of researchers and industry partners from other disciplines, where behavioral data offers similar opportunities. The availability of behavioral data is changing the way academia and industry access and leverage data to generate knowledge. This SIG is thus also relevant to a broad spectrum of disciplines, including social sciences, anthropology, psychology, interaction design, computer science, human-computer interaction, and engineering. Finally, we aim to connect and engage with regulatory bodies such as ethics committee members and data privacy officers.

We recognize an urgent need for strengthening the international, multi-disciplinary community across academia and industry focusing on the use of behavioral data throughout the design process of product-service systems. We believe that CHI is the venue that fits this ambition due to its scale, interdisciplinary community, and inclusive environment.

Going Further: Next Step

CHI ’23 is a milestone in developing an international community on Human-Centered Design with data across design researchers and practitioners. Beyond this event, we aim to:

• Iterate on and publish a Data-Centric Design research agenda.

• Continue monthly conversations and intensify interactions and collaborations through Slack.

• Initiate the organization of an annual, international symposium on Data-Centric Design using the research agenda as scope.

References

Bae, S. Sandra, Clement Zheng, Mary Etta West, Ellen Yi-Luen Do, Samuel Huron, and Danielle Albers Szafir. 2022. “Making Data Tangible: A Cross-disciplinary Design Space for Data Physicalization.” http://arxiv.org/abs/2202.10520.

Berger, Arne, Andreas Bischof, Sören Totzauer, Michael Storz, Kevin Lefeuvre, and Albrecht Kurze. 2018. “Sensing Home: Participatory Exploration of Smart Sensors in the Home.” In Social Internet of Things, edited by Alessandro Soro, Margot Brereton, and Paul Roe. Springer. https://doi.org/10.1007/978-3-319-94659-7.

Bogers, Sander, Joep Frens, Janne van Kollenburg, Eva Deckers, and Caroline Hummels. 2016. “Connected Baby Bottle.” In Proceedings of the 2016 ACM Conference on Designing Interactive Systems, 301–11. New York, NY, USA: ACM. https://doi.org/10.1145/2901790.2901855.

Bourgeois, J., J. Van Der Linden, G. Kortuem, B. A. Price, and C. Rimmer. 2014. “Using participatory data analysis to understand social constraints and opportunities of electricity demand-shifting.” In ICT for Sustainability 2014, ICT4S 2014, 392–401. Stockholm, Sweden: Atlantis Press. https://doi.org/10.2991/ict4s-14.2014.49.

Bowyer, Alex, Jack Holt, Josephine Go Jefferies, Rob Wilson, David Kirk, and Jan David Smeddinck. 2022. “Human-GDPR Interaction: Practical Experiences of Accessing Personal Data.” In CHI Conference on Human Factors in Computing Systems, 1–19. New York, NY, USA: ACM. https://doi.org/10.1145/3491102.3501947.

Clarke, Charlotte L., Heather Wilkinson, Julie Watson, Jane Wilcockson, Lindsay Kinnaird, and Toby Williamson. 2018. “A Seat Around the Table: Participatory Data Analysis With People Living With Dementia.” Qualitative Health Research 28 (9): 1421–33. https://doi.org/10.1177/1049732318774768.

D’Ignazio, Catherine, and Lauren F. Klein. 2020. Data Feminism. MIT Press. https://doi.org/https://doi.org/10.7551/mitpress/11805.001.0001.

Gomez Ortega, Alejandra, Janne Van Kollenburg, Yvette Shen, Dave Murray-rust, Dajana Nedic, Juan Jimenez Garcia, Wo Meijer, Pranshu Kumar Chaudhary, and Jacky Bourgeois. 2022. “SIG on Data as Human-Centered Design Material.” In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems, 4. New Orleans, LA, USA: Association for Computing Machinery (ACM). https://doi.org/10.1145/3491101.3516403.

Gómez Ortega, Alejandra, Jacky Bourgeois, and Gerd Kortuem. 2022. “Reconstructing Intimate Contexts through Data Donation: A Case Study in Menstrual Tracking Technologies.” In Nordic Human-Computer Interaction Conference, 1–12. New York, NY, USA: ACM. https://doi.org/10.1145/3546155.3546646.

Gorkovenko, Katerina, Daniel J Burnett, James K Thorp, Daniel Richards, and Dave Murray-Rust. 2020. “Exploring the Future of Data-Driven Product Design.” In Conference on Human Factors in Computing Systems - Proceedings, 1–14. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3313831.3376560.

Gorkovenko, Katerina, Daniel J Burnett, James Thorp, Daniel Richards, and Dave Murray-Rust. 2019. “Supporting Real-Time Contextual Inquiry Through Sensor Data Supporting Real-Time Contextual Inquiry Through Sensor Data.” In Ethnographic Praxis in Industry Conference Proceedings, 1–29. Edinburgh, UK.

Jofre, Ana, Steve Szigeti, Stephen Tiefenbach Keller, Lan-Xi Dong, David Czarnowski, Frederico Tomé, and Sara Diamond. n.d. “A Tangible User Interface for Interactive Data Visualization.”

Kollenburg, Janne van, and Sander Bogers. 2019. “Data-enabled design : a situated design approach that uses data as creative material when designing for intelligent ecosystems.” 2019. Eindhoven, The Netherlands: Eindhoven University of Technology. https://research.tue.nl/en/publications/data-enabled-design-a-situated-design-approach-that-uses-data-as-.

Kurze, Albrecht, Andreas Bischof, Sören Totzauer, Michael Storz, Maximilian Eibl, Margot Brereton, and Arne Berger. 2020. “Guess the Data: Data Work to Understand How People Make Sense of and Use Simple Sensor Data from Homes.” In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–12. New York, NY, USA: ACM. https://doi.org/10.1145/3313831.3376273.

Lovei, Peter, Renee Noortman, and Mathias Funk. 2022. “Introduction to Data-Enabled Design.” In Conference on Human Factors in Computing Systems - Proceedings, 1–3. Association for Computing Machinery (ACM). https://doi.org/10.1145/3491101.3503749.

Lovei, Peter, Renee Noortman, Sujithra Raviselvam, and Mathias Funk. 2022. Introduction to Data-Enabled Design. Vol. 1. 1. Association for Computing Machinery. https://doi.org/10.1145/3547522.3558899.

Lu, Jiahao, Alejandra Gomez Ortega, Milene Gonçalves, and Jacky Bourgeois. 2021. “the Impact of Data on the Role of Designers and Their Process.” Proceedings of the Design Society 1 (August): 3021–30. https://doi.org/10.1017/pds.2021.563.

Pins, Dominik, Timo Jakobi, Alexander Boden, Fatemeh Alizadeh, and Volker Wulf. 2021. “Alexa, We Need to Talk: A Data Literacy Approach on Voice Assistants.” In Designing Interactive Systems Conference 2021, 495–507. New York, NY, USA: ACM. https://doi.org/10.1145/3461778.3462001.

Tolmie, Peter, Andy Crabtree, Tom Rodden, James Colley, and Ewa Luger. 2016. “‘This has to be the cats’ - Personal Data Legibility in Networked Sensing Systems.” In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, 491–502. CSCW ’16. New York, NY, USA: ACM. https://doi.org/10.1145/2818048.2819992.

Wun, Tiffany, Lora Oehlberg, Miriam Sturdee, and Sheelagh Carpendale. 2019. “You say potato, I say po-data: Physical template tools for authoring visualizations.” In TEI 2019 - Proceedings of the 13th International Conference on Tangible, Embedded, and Embodied Interaction, 297–306. Association for Computing Machinery, Inc. https://doi.org/10.1145/3294109.3295627.

Young, Emma, Alan Marsden, and Paul Coulton. 2019. “Making the invisible audible: Sonifying qualitative data.” In ACM International Conference Proceeding Series, 124–30. Association for Computing Machinery (ACM). https://doi.org/10.1145/3356590.3356610.