  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.
