We are in the process of curating a list of this year’s publications — including links to social media, lab websites, and supplemental material. Currently, we have 68 full papers, 23 LBWs, three Journal papers, one alt.chi paper, two SIG, two Case Studies, one Interactivity, one Student Game Competition, and we lead three workshops. One paper received a best paper award and 13 papers received an honorable mention.
Disclaimer: This list is not complete yet; the DOIs might not be working yet.
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“Create a Fear of Missing Out” — ChatGPT Implements Unsolicited Deceptive Designs in Generated Websites Without Warning
Veronika Krauß (Technical University of Darmstadt), Mark McGill (University of Glasgow), Thomas Kosch (Humboldt University of Berlin), Yolanda Thiel (Technical University of Darmstadt), Dominik Schön (Technical University of Darmstadt), Jan Gugenheimer (Technical University of Darmstadt)
Honorable MentionAbstract | Tags: Dark Patterns, Full Paper, Honorable Mention | Links:
@inproceedings{Krau2025CreateFear,
title = {“Create a Fear of Missing Out” — ChatGPT Implements Unsolicited Deceptive Designs in Generated Websites Without Warning},
author = {Veronika Krauß (Technical University of Darmstadt), Mark McGill (University of Glasgow), Thomas Kosch (Humboldt University of Berlin), Yolanda Thiel (Technical University of Darmstadt), Dominik Schön (Technical University of Darmstadt), Jan Gugenheimer (Technical University of Darmstadt)},
url = {https://www.teamdarmstadt.de/, website
www.linkedin.com/in/veronikakrauss, linkedin},
doi = {10.1145/3706598.3713083},
year = {2025},
date = {2025-04-26},
urldate = {2025-04-26},
abstract = {With the recent advancements in Large Language Models (LLMs), web developers increasingly apply their code-generation capabilities to website design. However, since these models are trained on existing designerly knowledge, they may inadvertently replicate bad or even illegal practices, especially deceptive designs (DD). This paper examines whether users can accidentally create DD for a fictitious webshop using GPT-4. We recruited 20 participants, asking them to use ChatGPT to generate functionalities (product overview or checkout) and then modify these using neutral prompts to meet a business goal (e.g., „increase the likelihood of us selling our product“). We found that all 20 generated websites contained at least one DD pattern (mean: 5, max: 9), with GPT-4 providing no warnings. When reflecting on the designs, only 4 participants expressed concerns, while most considered the outcomes satisfactory and not morally problematic, despite the potential ethical and legal implications for end-users and those adopting ChatGPT's recommendations.},
keywords = {Dark Patterns, Full Paper, Honorable Mention},
pubstate = {published},
tppubtype = {inproceedings}
}
With the recent advancements in Large Language Models (LLMs), web developers increasingly apply their code-generation capabilities to website design. However, since these models are trained on existing designerly knowledge, they may inadvertently replicate bad or even illegal practices, especially deceptive designs (DD). This paper examines whether users can accidentally create DD for a fictitious webshop using GPT-4. We recruited 20 participants, asking them to use ChatGPT to generate functionalities (product overview or checkout) and then modify these using neutral prompts to meet a business goal (e.g., „increase the likelihood of us selling our product“). We found that all 20 generated websites contained at least one DD pattern (mean: 5, max: 9), with GPT-4 providing no warnings. When reflecting on the designs, only 4 participants expressed concerns, while most considered the outcomes satisfactory and not morally problematic, despite the potential ethical and legal implications for end-users and those adopting ChatGPT's recommendations.
A Comparative Study of How People With and Without ADHD Recognise and Avoid Dark Patterns on Social Media
Thomas Mildner (University of Bremen), Daniel Fidel (University of Bremen), Evropi Stefanidi (University of Bremen, TU Wien), Paweł W. Woźniak (TU Wien), Rainer Malaka (University of Bremen), Jasmin Niess (University of Oslo)
Abstract | Tags: Case Study, Dark Patterns, Full Paper | Links:
@inproceedings{Mildner2025ComparativeStudy,
title = {A Comparative Study of How People With and Without ADHD Recognise and Avoid Dark Patterns on Social Media},
author = {Thomas Mildner (University of Bremen), Daniel Fidel (University of Bremen), Evropi Stefanidi (University of Bremen, TU Wien), Paweł W. Woźniak (TU Wien), Rainer Malaka (University of Bremen), Jasmin Niess (University of Oslo)},
url = {dm.tzi.de, website
https://de.linkedin.com/company/dml-bremen, research group linkedin
https://www.linkedin.com/in/mildner-thomas/, author\'s linkedin},
doi = {10.1145/3706598.3713776},
year = {2025},
date = {2025-04-26},
urldate = {2025-04-26},
abstract = {Dark patterns are deceptive strategies that recent work in humancomputer interaction (HCI) has captured throughout digital domains, including social networking sites (SNSs). While research has identified difficulties among people to recognise dark patterns effectively, few studies consider vulnerable populations and their experience in this regard, including people with attention deficit hyperactivity disorder (ADHD), who may be especially susceptible to attention-grabbing tricks. Based on an interactive web study with 135 participants, we investigate SNS users’ ability to recognise and avoid dark patterns by comparing results from participants with and without ADHD. In line with prior work, we noticed overall low recognition of dark patterns with no significant differences between the two groups. Yet, ADHD individuals were able to avoid specific dark patterns more often. Through an interactive study, we expand previous work by understanding dark patterns in a realistic environment and offer insights into their effect on vulnerable populations.},
keywords = {Case Study, Dark Patterns, Full Paper},
pubstate = {published},
tppubtype = {inproceedings}
}
Dark patterns are deceptive strategies that recent work in humancomputer interaction (HCI) has captured throughout digital domains, including social networking sites (SNSs). While research has identified difficulties among people to recognise dark patterns effectively, few studies consider vulnerable populations and their experience in this regard, including people with attention deficit hyperactivity disorder (ADHD), who may be especially susceptible to attention-grabbing tricks. Based on an interactive web study with 135 participants, we investigate SNS users’ ability to recognise and avoid dark patterns by comparing results from participants with and without ADHD. In line with prior work, we noticed overall low recognition of dark patterns with no significant differences between the two groups. Yet, ADHD individuals were able to avoid specific dark patterns more often. Through an interactive study, we expand previous work by understanding dark patterns in a realistic environment and offer insights into their effect on vulnerable populations.
Getting Trapped in Amazon’s “Iliad Flow”: A Foundation for the Temporal Analysis of Dark Patterns
Colin M. Gray (Indiana University), Thomas Mildner (University of Bremen), Ritiga Gairola (Indiana University)
Abstract | Tags: Dark Patterns, Full Paper | Links:
@inproceedings{Gray2025GettingTrapped,
title = {Getting Trapped in Amazon’s “Iliad Flow”: A Foundation for the Temporal Analysis of Dark Patterns},
author = {Colin M. Gray (Indiana University), Thomas Mildner (University of Bremen), Ritiga Gairola (Indiana University)},
url = {dm.tzi.de, website
https://www.linkedin.com/company/dml-bremen/, research group linkedin
https://www.linkedin.com/in/mildner-thomas/, author's linkedin},
doi = {10.1145/3706598.3713828},
year = {2025},
date = {2025-04-26},
urldate = {2025-04-26},
abstract = {Dark patterns are ubiquitous in digital systems, impacting users throughout their journeys on many popular apps and websites. While substantial efforts from the research community in the last five years have led to consolidated taxonomies and an ontology of dark patterns, most characterizations of these patterns have been focused on static images or isolated pattern types. In this paper, we leverage documents from a US Federal Trade Commission complaint describing dark patterns in Amazon Prime's ``Iliad Flow,'' illustrating the interplay of dark patterns across a user journey. We use this case study to illustrate how dark patterns can be characterized and mapped over time, providing a sufficient audit trail and consistent application of dark patterns at high- and meso-level scales. We conclude by describing the groundwork for a methodology of Temporal Analysis of Dark Patterns (TADP) that allows for rigorous identification of dark patterns by researchers, regulators, and legal scholars.},
keywords = {Dark Patterns, Full Paper},
pubstate = {published},
tppubtype = {inproceedings}
}
Dark patterns are ubiquitous in digital systems, impacting users throughout their journeys on many popular apps and websites. While substantial efforts from the research community in the last five years have led to consolidated taxonomies and an ontology of dark patterns, most characterizations of these patterns have been focused on static images or isolated pattern types. In this paper, we leverage documents from a US Federal Trade Commission complaint describing dark patterns in Amazon Prime's ``Iliad Flow,'' illustrating the interplay of dark patterns across a user journey. We use this case study to illustrate how dark patterns can be characterized and mapped over time, providing a sufficient audit trail and consistent application of dark patterns at high- and meso-level scales. We conclude by describing the groundwork for a methodology of Temporal Analysis of Dark Patterns (TADP) that allows for rigorous identification of dark patterns by researchers, regulators, and legal scholars.
What Makes XR Dark? Examining Emerging Dark Patterns in Augmented and Virtual Reality through Expert Co-Design
Veronika Krauß (University of Michigan, University of Applied Sciences Bonn-Rhein-Sieg), Pejman Saeghe (University of Strathclyde), Alexander Boden (University of Applied Sciences Bonn-Rhein-Sieg), Mohamed Khamis (University of Glasgow), Mark McGill (University of Glasgow), Jan Gugenheimer (Technical University of Darmstadt), Michael Nebeling (University of Michigan)
Abstract | Tags: Dark Patterns, Journal | Links:
@inproceedings{Krau2025WhatMakes,
title = {What Makes XR Dark? Examining Emerging Dark Patterns in Augmented and Virtual Reality through Expert Co-Design},
author = {Veronika Krauß (University of Michigan and University of Applied Sciences Bonn-Rhein-Sieg), Pejman Saeghe (University of Strathclyde), Alexander Boden (University of Applied Sciences Bonn-Rhein-Sieg), Mohamed Khamis (University of Glasgow), Mark McGill (University of Glasgow), Jan Gugenheimer (Technical University of Darmstadt), Michael Nebeling (University of Michigan)},
url = {https://www.verbraucherinformatik.de, website
https://www.linkedin.com/groups/9076152/, research group linkedin
https://www.linkedin.com/in/veronikakrauss/, author's linkedin},
doi = {10.1145/3660340},
year = {2025},
date = {2025-04-26},
urldate = {2025-04-26},
abstract = {Dark patterns are deceptive designs that influence a user’s interactions with an interface to benefit someone other than the user. Prior work has identified dark patterns in windows, icons, menus, and pointer (WIMP) interfaces and ubicomp environments, but how dark patterns can manifest in Augmented and Virtual Reality (collectively XR) requires more attention. We therefore conducted 10 co-design workshops with 20 experts in XR and deceptive design. Our participants co-designed 42 scenarios containing dark patterns, based on application archetypes presented in recent HCI/XR literature. In the co-designed scenarios, we identified 10 novel dark patterns in addition to 39 existing ones, as well as 10 examples in which specific characteristics associated with XR potentially amplified the effect dark patterns could have on users. Based on our findings and prior work, we present a classification of XR-specific properties that facilitate dark patterns: perception, spatiality, physical/virtual barriers, and XR device sensing. We also present the experts’ assessments of the likelihood and severity of the co-designed scenarios and highlight key aspects they considered for this evaluation, for example, technological feasibility, ease of upscaling and distributing malicious implementations, and the application’s context of use. Finally, we discuss means to mitigate XR dark patterns and support regulatory bodies to reduce potential harms.},
keywords = {Dark Patterns, Journal},
pubstate = {published},
tppubtype = {inproceedings}
}
Dark patterns are deceptive designs that influence a user’s interactions with an interface to benefit someone other than the user. Prior work has identified dark patterns in windows, icons, menus, and pointer (WIMP) interfaces and ubicomp environments, but how dark patterns can manifest in Augmented and Virtual Reality (collectively XR) requires more attention. We therefore conducted 10 co-design workshops with 20 experts in XR and deceptive design. Our participants co-designed 42 scenarios containing dark patterns, based on application archetypes presented in recent HCI/XR literature. In the co-designed scenarios, we identified 10 novel dark patterns in addition to 39 existing ones, as well as 10 examples in which specific characteristics associated with XR potentially amplified the effect dark patterns could have on users. Based on our findings and prior work, we present a classification of XR-specific properties that facilitate dark patterns: perception, spatiality, physical/virtual barriers, and XR device sensing. We also present the experts’ assessments of the likelihood and severity of the co-designed scenarios and highlight key aspects they considered for this evaluation, for example, technological feasibility, ease of upscaling and distributing malicious implementations, and the application’s context of use. Finally, we discuss means to mitigate XR dark patterns and support regulatory bodies to reduce potential harms.