@inproceedings{datta-etal-2022-greasevision-rewriting,
title = "{G}rease{V}ision: Rewriting the Rules of the Interface",
author = "Datta, Siddhartha and
Kollnig, Konrad and
Shadbolt, Nigel",
editor = "Narang, Kanika and
Mostafazadeh Davani, Aida and
Mathias, Lambert and
Vidgen, Bertie and
Talat, Zeerak",
booktitle = "Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH)",
month = jul,
year = "2022",
address = "Seattle, Washington (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.woah-1.3",
doi = "10.18653/v1/2022.woah-1.3",
pages = "24--28",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T GreaseVision: Rewriting the Rules of the Interface
%A Datta, Siddhartha
%A Kollnig, Konrad
%A Shadbolt, Nigel
%Y Narang, Kanika
%Y Mostafazadeh Davani, Aida
%Y Mathias, Lambert
%Y Vidgen, Bertie
%Y Talat, Zeerak
%S Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington (Hybrid)
%F datta-etal-2022-greasevision-rewriting
%X 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.
%R 10.18653/v1/2022.woah-1.3
%U https://aclanthology.org/2022.woah-1.3
%U https://doi.org/10.18653/v1/2022.woah-1.3
%P 24-28
Markdown (Informal)
[GreaseVision: Rewriting the Rules of the Interface](https://aclanthology.org/2022.woah-1.3) (Datta et al., WOAH 2022)
ACL
- Siddhartha Datta, Konrad Kollnig, and Nigel Shadbolt. 2022. GreaseVision: Rewriting the Rules of the Interface. In Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH), pages 24–28, Seattle, Washington (Hybrid). Association for Computational Linguistics.