Abstract
Digital harms can manifest across any interface. Key problems in addressing these harms include the high individuality of harms and the fast-changing nature of digital systems. We put forth GreaseVision, a collaborative human-in-the-loop learning framework that enables end-users to analyze their screenomes to annotate harms as well as render overlay interventions. We evaluate HITL intervention development with a set of completed tasks in a cognitive walkthrough, and test scalability with one-shot element removal and fine-tuning hate speech classification models. The contribution of the framework and tool allow individual end-users to study their usage history and create personalized interventions. Our contribution also enables researchers to study the distribution of multi-modal harms and interventions at scale.
Original language | English |
---|---|
Title of host publication | DADC 2022 - 1st Workshop on Dynamic Adversarial Data Collection, Proceedings of the Workshop |
Editors | Max Bartolo, Hannah Rose Kirk, Pedro Rodriguez, Katerina Margatina, Tristan Thrush, Robin Jia, Pontus Stenetorp, Adina Williams, Douwe Kiela |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 7-22 |
Number of pages | 16 |
ISBN (Electronic) | 9781955917940 |
Publication status | Published - 2022 |
Externally published | Yes |
Event | 1st Workshop on Dynamic Adversarial Data Collection, DADC 2022 - Seattle, United States Duration: 14 Jul 2022 → 14 Jul 2022 https://dadcworkshop.github.io/ |
Publication series
Series | Workshop on Dynamic Adversarial Data Collection. Proceedings |
---|
Workshop
Workshop | 1st Workshop on Dynamic Adversarial Data Collection, DADC 2022 |
---|---|
Country/Territory | United States |
City | Seattle |
Period | 14/07/22 → 14/07/22 |
Internet address |