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Published, Version of Record (VoR)Open Access via Drexel Libraries Read and Publish Program 2025CC BY V4.0, Open
Conference proceeding
Designing for Difference: How We Learn to Stop Worrying and Love the Doppelganger
Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, pp 1-15
26 Apr 2025
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Abstract
To use social media is to interact with digital representations of oneself in the form of algorithmically-determined personalized content. Yet when we assume that interactions with personalized content will be a persistent feature of our futures, the concepts available to frame such digital representations – things variously called doubles, twins, and doppelgangers – appear as worryingly creepy. Where might one find optimism amid such presumptive creepiness? Through conceptual analysis of data doubles, digital twins, and data doppelgangers, we identify and explain one source of justifiable optimism. Unlike the double and twin, the data doppelganger’s dynamics center difference rather than presumed sameness. Fostering justifiable optimism about the futures of personalization – with social media as a starting point – requires learning how to design for the experience of difference represented by the doppelganger: the irreducibility of the person to the represented user.
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Details
- Title
- Designing for Difference: How We Learn to Stop Worrying and Love the Doppelganger
- Creators
- John S. Seberger (Corresponding Author) - Drexel UniversitySanonda Datta Gupta - Drexel University
- Publication Details
- Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, pp 1-15
- Conference
- CHI 2025: CHI Conference on Human Factors in Computing Systems
- Series
- ACM Conferences
- Publisher
- Association for Computing Machinery
- Number of pages
- 15
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science; Center for Science, Technology, and Society
- Web of Science ID
- WOS:001501406100060
- Scopus ID
- 2-s2.0-105005750832
- Other Identifier
- 991022048905804721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Computer Science, Theory & Methods
- Robotics