Creative Data Work in the Design Process

Kun, Péter; Mulder, Ingrid; Götzen, Amalia de; Kortuem, Gerd

doi:10.1145/3325480.3325500

Cited by 16 publications

(10 citation statements)

References 35 publications

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“…Other design challenges of AI UX discussed in the literature include a shift towards data-driven design culture [17,36], challenges to prototype and engage in quick design iterations [13,50,53,59], and the needs to engage stakeholders to align the values of AI system [38,60]. Our work will primarily tackle supporting a "designerly" understanding on the technical space of XAI, and designer-AI engineer collaboration, as discussed in the requirements below.…”

Section: Ioutputmentioning

confidence: 99%

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Questioning the AI: Informing Design Practices for Explainable AI User Experiences

Liao

Gruen

Miller

2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

398

202

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A pervasive design issue of AI systems is their explainability-how to provide appropriate information to help users understand the AI.The technical field of explainable AI (XAI) has produced a rich toolbox of techniques. Designers are now tasked with the challenges of how to select the most suitable XAI techniques and translate them into UX solutions. Informed by our previous work studying design challenges around XAI UX, this work proposes a design process to tackle these challenges. We review our and related prior work to identify requirements that the process should fulfill, and accordingly, propose a Question-Driven Design Process that grounds the user needs, choices of XAI techniques, design, and evaluation of XAI UX all in the user questions. We provide a mapping guide between prototypical user questions and exemplars of XAI techniques, serving as boundary objects to support collaboration between designers and AI engineers. We demonstrate it with a use case of designing XAI for healthcare adverse events prediction, and discuss lessons learned for tackling design challenges of AI systems.CCS Concepts: • Human-centered computing → Interaction design process and methods; • Computing methodologies → Artificial intelligence.

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Section: Ioutputmentioning

confidence: 99%

“…Other design challenges of AI UX discussed in the literature include a shift towards data-driven design culture [17,36],…”

Section: Ioutputmentioning

confidence: 99%

Questioning the AI: Informing Design Practices for Explainable AI User Experiences

Liao

Gruen

Miller

2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

398

202

View full text Add to dashboard Cite

show abstract

“…Designers of many kinds (Coulton and Lindley, 2019;Gorkovenko et al, 2020) and their supporting industries (Raustiala and Sprigman, 2019) have well understood the value that can be derived from using data in their creative and innovation activities (Varshney et al, 2013;Rousseaux, 2017;Chaudhuri and Koltun, 2010). This phenomenon is eminently observable in large-scale industrial settings where products and services are underpinned by datasets of often inordinate size, captured en masse in quantitative form, with a heterogeneous composition and lack of context that requires considerable data work to turn into actionable information (Kun et al, 2019(Kun et al, , 2020. Mass-scale data use is also observable in scientific contexts, with researchers leveraging sizeable open datasets, or utilizing data capture techniques, including scraping digital footprints from social media such as Twitter (Lin and Ryaboy, 2013;Steinert-Threlkeld, 2018;Zhang et al, 2018).…”

Section: Reviewing Data Use In Museums and Galleriesmentioning

confidence: 99%

Data-inspired co-design for museum and gallery visitor experiences

Darzentas

Cameron

Wagner

et al. 2022

AIEDAM

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The capture and analysis of diverse data is widely recognized as being vital to the design of new products and services across the digital economy. We focus on its use to inspire the co-design of visitor experiences in museums as a distinctive case that reveals opportunities and challenges for the use of personal data. We present a portfolio of data-inspired visiting experiences that emerged from a 3-year Research Through Design process. These include the overlay of virtual models on physical exhibits, a smartphone app for creating personalized tours as gifts, visualizations of emotional responses to exhibits, and the data-driven use of ideation cards. We reflect across our portfolio to articulate the diverse ways in which data can inspire design through the use of ambiguity, visualization, and inter-personalization; how data inspire co-design through the process of co-ideation, co-creation, and co-interpretation; and how its use must negotiate the challenges of privacy, ownership, and transparency. By adopting a human perspective on data, we are able to chart out the complex and rich information that can inform design activities and contribute to datasets that can drive creativity support systems.

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“…To conclude, while the big data era has been triggering new approaches to inquiry in various fields, including design, designers still primarily approach data through data expert collaborators. Contrary to the previous approaches relying on data experts in the process, our previous work [18,19] has explored how designers as 'data non-experts' can leverage data. Our investigations confirm that new types of insights can be gained from 'big data approaches' to fuel the design process, and designers themselves are able to conduct data practices using non-expert tooling.…”

Section: Introductionmentioning

confidence: 99%

Developing a Design Inquiry Method for Data Exploration

Kun¹,

Mulder²,

Kortuem³

2020

IxD&A

Self Cite

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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-an-end, and designers being part of the data collection.

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Creative Data Work in the Design Process

Cited by 16 publications

References 35 publications

Questioning the AI: Informing Design Practices for Explainable AI User Experiences

Questioning the AI: Informing Design Practices for Explainable AI User Experiences

Data-inspired co-design for museum and gallery visitor experiences

Developing a Design Inquiry Method for Data Exploration

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