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Title: Autospeculation: Reflecting on the Intimate and Imaginative Capacities of Data Analysis
Given decades of Human computer interaction (HCI) research focused on scientific empiricism, it can be hard for the field to acknowledge that data analysis is both an emotional and speculative process. But what does it mean for this process of data analysis to embrace its situated and speculative nature? In this paper, we explore this possibility by building on decades of HCI mixed methods that root data analysis in design. Drawing on an autoethnographic design inquiry, we examine how data analysis can work as an implicating process, one that is not only critically grounded in a designer’s own situation but also offers modes of imagining the world otherwise. In this analysis, we find that autobiographical design can help HCI scholars to respond to current critiques of speculative design by grounding and rendering more personal certain kinds of speculation, opening a space for diverse voices to emerge.  more » « less
Award ID(s):
1947696 2222242
PAR ID:
10465940
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
Page Range / eLocation ID:
1 to 10
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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