@inproceedings{datta-etal-2022-greasevision,
title = "{G}rease{V}ision: Rewriting the Rules of the Interface",
author = "Datta, Siddhartha and
Kollnig, Konrad and
Shadbolt, Nigel",
editor = "Bartolo, Max and
Kirk, Hannah and
Rodriguez, Pedro and
Margatina, Katerina and
Thrush, Tristan and
Jia, Robin and
Stenetorp, Pontus and
Williams, Adina and
Kiela, Douwe",
booktitle = "Proceedings of the First Workshop on Dynamic Adversarial Data Collection",
month = jul,
year = "2022",
address = "Seattle, WA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.dadc-1.2",
doi = "10.18653/v1/2022.dadc-1.2",
pages = "7--22",
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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="datta-etal-2022-greasevision">
<titleInfo>
<title>GreaseVision: Rewriting the Rules of the Interface</title>
</titleInfo>
<name type="personal">
<namePart type="given">Siddhartha</namePart>
<namePart type="family">Datta</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Konrad</namePart>
<namePart type="family">Kollnig</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nigel</namePart>
<namePart type="family">Shadbolt</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the First Workshop on Dynamic Adversarial Data Collection</title>
</titleInfo>
<name type="personal">
<namePart type="given">Max</namePart>
<namePart type="family">Bartolo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hannah</namePart>
<namePart type="family">Kirk</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Pedro</namePart>
<namePart type="family">Rodriguez</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Katerina</namePart>
<namePart type="family">Margatina</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tristan</namePart>
<namePart type="family">Thrush</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Robin</namePart>
<namePart type="family">Jia</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Pontus</namePart>
<namePart type="family">Stenetorp</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Adina</namePart>
<namePart type="family">Williams</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Douwe</namePart>
<namePart type="family">Kiela</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Seattle, WA</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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>
<identifier type="citekey">datta-etal-2022-greasevision</identifier>
<identifier type="doi">10.18653/v1/2022.dadc-1.2</identifier>
<location>
<url>https://aclanthology.org/2022.dadc-1.2</url>
</location>
<part>
<date>2022-07</date>
<extent unit="page">
<start>7</start>
<end>22</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T GreaseVision: Rewriting the Rules of the Interface
%A Datta, Siddhartha
%A Kollnig, Konrad
%A Shadbolt, Nigel
%Y Bartolo, Max
%Y Kirk, Hannah
%Y Rodriguez, Pedro
%Y Margatina, Katerina
%Y Thrush, Tristan
%Y Jia, Robin
%Y Stenetorp, Pontus
%Y Williams, Adina
%Y Kiela, Douwe
%S Proceedings of the First Workshop on Dynamic Adversarial Data Collection
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, WA
%F datta-etal-2022-greasevision
%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.dadc-1.2
%U https://aclanthology.org/2022.dadc-1.2
%U https://doi.org/10.18653/v1/2022.dadc-1.2
%P 7-22
Markdown (Informal)
[GreaseVision: Rewriting the Rules of the Interface](https://aclanthology.org/2022.dadc-1.2) (Datta et al., DADC 2022)
ACL
- Siddhartha Datta, Konrad Kollnig, and Nigel Shadbolt. 2022. GreaseVision: Rewriting the Rules of the Interface. In Proceedings of the First Workshop on Dynamic Adversarial Data Collection, pages 7–22, Seattle, WA. Association for Computational Linguistics.