With or Without Permission: Site-Specific Augmented Reality for Social Justice
May 06, 2024 Β· Declared Dead Β· π CHI Extended Abstracts
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Rafael M. L. Silva, Ana MarΓa CΓ‘rdenas Gasca, Joshua A. Fisher, Erica Principe Cruz, Cinthya Jauregui, Amy Lueck, Fannie Liu, AndrΓ©s Monroy-HernΓ‘ndez, Kai Lukoff
arXiv ID
2405.03898
Category
cs.HC: Human-Computer Interaction
Citations
10
Venue
CHI Extended Abstracts
Last Checked
4 months ago
Abstract
Movements for social change are often tied to a particular locale. This makes Augmented Reality (AR), which changes how people perceive their surroundings, a promising technology for social justice. Site-specific AR empowers activists to re-tell the story of a place, with or without permission of its owner. It has been used, for example, to reveal hidden histories, re-imagine problematic monuments, and celebrate minority cultures. However, challenges remain concerning technological ownership and accessibility, scalability, sustainability, and navigating collaborations with marginalized communities and across disciplinary boundaries. This half-day workshop at CHI 2024 seeks to bring together an interdisciplinary group of activists, computer scientists, designers, media scholars, and more to identify opportunities and challenges across domains. To anchor the discussion, participants will each share one example of an artifact used in speculating, designing, and/or delivering site-specific AR experiences. This collection of artifacts will inaugurate an interactive database that can inspire a new wave of activists to leverage AR for social justice.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted