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. As a result, we still lack a systematic approach to study harms and produce interventions for end-users. We put forward GreaseVision, a new framework that enables end-users to collaboratively develop interventions against harms in software using a no-code approach and recent advances in few-shot machine learning. 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 harms and interventions at scale.
@article{arxiv.2204.03731,
title = {GreaseVision: Rewriting the Rules of the Interface},
author = {Siddhartha Datta and Konrad Kollnig and Nigel Shadbolt},
journal= {arXiv preprint arXiv:2204.03731},
year = {2022}
}