Marie Younghui Kim is an interactive artist, wearable HCI researcher and PhD candidate with the Urban Informatics Group at the QUT Design Lab. Her background is in visual arts and design, Interactive media, Physical Computing, Wearable Technology and Academic Teaching. She received her Bachelor of Fine Arts at Parsons School of Design in New York and her Master’s degree at ITP (Interactive Telecommunication Program), Tisch School of the Arts, New York University. Before joining QUT, she taught Interaction Design and Arts as an associate professor at College of Art and Design, Hongik University (S. Korea). Her most recent publication was “mood.cloud – data as art” at Interactive Art Gallery at ACM CHI 2015 along with Art.CHI workshop, and her most recent exhibition was on creative data sensemaking of virtual currency versus edible commodities, titled “Weight of Data” (Seoul, 2016).
- Creative Data Sensemaking through Artistic Practice
- Tangible, Embodied Interaction through Data Art
- Wearable Interaction and digital fabrication
- Kinetic, physical computing art and design
- Human-Computer Interaction
- Emotional Data in Public Creativity
Creative Data Sensemaking for Art Practice in the Age of Big Data
Collection and use of Big Data is now ubiquitous in everyday lives of many people along with the rapid growth of data-generating technologies. The potential opportunities of Big Data excites scientists, marketers, policymakers and others as resources to form understanding about particular people and the world but, also raises critical concerns such as misleading data representation, data bias, privacy issues, unequal accessibility and data transparency. In parallel, artistic exploration of Big Data can act as a critical commentary about and revelation of the current digitally saturated era. This process through which artists translate and make meanings from data by correlating or exploring data patterns conceptually for themselves and the audience is termed ‘creative data sensemaking’ in this study. Creative data sensemaking blurs the boundaries between scientific data and creative expression and presents new opportunities for artistic approaches toward Big Data. At the same time, challenges emerge because of the interdisciplinary nature of this practice. In this interdisciplinary practice-led research, I will develop methods and guidelines to help enable artists to better navigate the ambiguities and issues arising from the use of Big Data through my own reflective art practice that highlights understandings of both the nature of Big Data and the subjective ways that artists may work with them.