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Sarah Sahabi

dblp:354/7694 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
0000-0002-9169-3230ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 2 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Usability and user experience research · 77% User interface design and tools · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Usability and user experience research
dark pattern detection
0.812024
Fighting Malicious Designs: Towards Visual Countermeasures Against Dark Patterns · CHI 2024
User interface design and tools › interaction design
deceptive design
0.212024
Fighting Malicious Designs: Towards Visual Countermeasures Against Dark Patterns · CHI 2024

Methods — techniques the papers use, named apart from their topics

visual countermeasure design · 0.8
YearPublicationVenuePosition
2025 "If They Have No Choice, They'll Accept!": How Children and Adolescents Assess Deceptive Designs
abstract
Figure 1: In our study, children (top) and adolescents (bottom) redesigned fair versions of three diferent dialogs that originally asked users to accept notifcations (left), to accept cookies (center), or to watch an ad to continue playing (right).We asked participants to nudge users toward acceptance.Redesigns contained no option to decline (C19), visual nudging (A20), colorful designs (C25, A32), increased wait times (C10), and emotional manipulation (A11).English translations are provided in Figure 5.
René Schäfer, Sarah Sahabi, Lucia Karl, Sophie Hahn, Jan O. Borchers
IDC2
2024 Fighting Malicious Designs: Towards Visual Countermeasures Against Dark Patterns
abstract
Dark patterns are malicious UI design strategies that nudge users towards decisions going against their best interests. To create technical countermeasures against them, dark patterns must be automatically detectable. While researchers have devised algorithms to detect some patterns automatically, there has only been little work to use obtained results to technically counter the effects of dark patterns when users face them on their devices.
René Schäfer, Paul Miles Preuschoff, René Röpke, Sarah Sahabi, Jan O. Borchers
CHI4