- Title
- The Art of Data Portraiture: Enabling a Public Debate on Self-surveillance
- Creator
- Kenke, Ralph; Trefz, Elmar; Roxburgh, Mark; Minichiello, Mario
- Relation
- Art Machines 2: International Symposium on Machine Learning and Art 2021. Proceedings of Art Machines 2: International Symposium on Machine Learning and Art 2021 (Hong Kong 10-14 June, 2021) p. 63-77
- Publisher
- School of Creative Media, City University of Hong Kong
- Resource Type
- conference paper
- Date
- 2021
- Description
- This research is a practice-based speculative design inquiry into the emerging field of data portraiture. Humans’ use of the networked digital environments that are now so much part of life leaves a massive data trail stemming from individuals’ everyday interactions with these environments. An increasing amount of this data trail remains invisible. Although we spend a significant amount of time participating in digital network activities, we have just begun to discover the potential of visualizing personal data as a graphical representation. This research into the emerging field of data portraiture seeks to understand the role of the “artist” as a creative practitioner in interpreting qualitative data into image experiences, and to offer insights into the behaviour and interests of individuals engaging with such work. Through a number design iterations, this research investigates and reveals the importance of participant contribution to the “datafication” of social life and the emergence of “self-surveillance” that can shape data portraits.
- Subject
- digital environments; data portraiture; art; datafication of social life
- Identifier
- http://hdl.handle.net/1959.13/1435612
- Identifier
- uon:39772
- Identifier
- ISBN:9789624424485
- Language
- eng
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