“It’s Not What We Were Trying to Get At, but I Think Maybe It Should Be”: Learning How to Do Trauma-Informed Design with a Data Donation Platform for Online Dating Sexual Violence

Zheng, Wenqi; Walquist, Emma; Datey, Isha; Zhou, Xiangyu; Berishaj, Kelly; Mcdonald, Melissa; Parkhill, Michele; Zhu, Dongxiao; Zytko, Douglas

doi:10.1145/3613904.3642045

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Other1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Undercover Assistance: Designing a Disguised App to Navigate Sexual Harassment

Nigri,

Kang

2024

The 37th Annual ACM Symposium on User Interface Software and Technology

View full text Add to dashboard Cite

Undercover Assistance: Designing a Disguised App to Navigate Sexual Harassment

Nigri,

Kang

2024

The 37th Annual ACM Symposium on User Interface Software and Technology

View full text Add to dashboard Cite

"Just Like, Risking Your Life Here": Participatory Design of User Interactions with Risk Detection AI to Prevent Online-to-Offline Harm Through Dating Apps

Datey,

Zytko

2024

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

Social computing platforms facilitate interpersonal harms that manifest across online and physical realms such as sexual violence between online daters and sexual grooming through social media. Risk detection AI has emerged as an approach to preventing such harms, however a myopic focus on computational performance has been criticized in HCI literature for failing to consider how users should interact with risk detection AI to stay safe. In this paper we report an interview study with woman-identifying online daters (n=20) about how they envision interacting with risk detection AI and how risk detection models can be designed pursuant to such interactions. In accordance with this goal, we engaged women in risk detection model building exercises to build their own risk detection models. Findings show that women anticipate interacting with risk detection AI to augment - not replace - their personal risk assessment strategies. They likewise designed risk detection models to amplify their subjective and admittedly biased indicators of risk. Design implications involve the notion of personalizable risk detection models, but also ethical concerns around perpetuating problematic stereotypes associated with risk.

show abstract

“It’s Not What We Were Trying to Get At, but I Think Maybe It Should Be”: Learning How to Do Trauma-Informed Design with a Data Donation Platform for Online Dating Sexual Violence

Cited by 2 publications

References 85 publications

Undercover Assistance: Designing a Disguised App to Navigate Sexual Harassment

Undercover Assistance: Designing a Disguised App to Navigate Sexual Harassment

"Just Like, Risking Your Life Here": Participatory Design of User Interactions with Risk Detection AI to Prevent Online-to-Offline Harm Through Dating Apps

Contact Info

Product

Resources

About