Abstract
Algorithms shape content consumption yet limit self-awareness by obscuring how they model users’ identities. To address this, we explored whether generative AI–based 3D object visualizations, created from individuals’ Netflix viewing histories, could support reflective engagement with algorithmic ally inferred preferences. Unlike traditional data visualizations, our approach external izes content consumption as metaphorical self-images, enabling users to interpret their algorithmic identities through visual cues. Through semi-structured interviews with ten participants, we found that AI- generated self-images revealed unconscious dispositions, evoked emotional responses, and encouraged rethinking of personal tastes. However, issues of contextual misalignment and interpretive ambiguity emerged when emotional or situational intent was absent from the input data. Our findings suggest that such visualizations can foster self-awareness by transforming behavioral data into expressive, interpret able forms. We outline design considerations to make AI-generated self-images more interpret able, engaging, and personally meaningful to better support algorithmic self-reflection.
Keywords
Algorithmic Self; Generative AI; Self-reflection; Data Visualization
DOI
https://doi.org/10.21606/iasdr.2025.941
Citation
Lee, H.,and Kim, D.(2025) What can AI-generated 3D objects reveal about myself? Exploring the potential of AI-generated self-images for critical reflection on content consumption shaped by Netflix algorithms, in Chang, C.-Y., and Hsu, Y. (eds.), IASDR 2025: Design Next, 02-05 December, Taiwan. https://doi.org/10.21606/iasdr.2025.941
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Track 4 - Human-Centered AI
What can AI-generated 3D objects reveal about myself? Exploring the potential of AI-generated self-images for critical reflection on content consumption shaped by Netflix algorithms
Algorithms shape content consumption yet limit self-awareness by obscuring how they model users’ identities. To address this, we explored whether generative AI–based 3D object visualizations, created from individuals’ Netflix viewing histories, could support reflective engagement with algorithmic ally inferred preferences. Unlike traditional data visualizations, our approach external izes content consumption as metaphorical self-images, enabling users to interpret their algorithmic identities through visual cues. Through semi-structured interviews with ten participants, we found that AI- generated self-images revealed unconscious dispositions, evoked emotional responses, and encouraged rethinking of personal tastes. However, issues of contextual misalignment and interpretive ambiguity emerged when emotional or situational intent was absent from the input data. Our findings suggest that such visualizations can foster self-awareness by transforming behavioral data into expressive, interpret able forms. We outline design considerations to make AI-generated self-images more interpret able, engaging, and personally meaningful to better support algorithmic self-reflection.