publications | Hauke G.W. Sandhaus

Under review 4

Persuading Metrics: How User Evaluation Data Shapes Professional Designers' Willingness to Reject Dark Patterns
Sandhaus, H., Rhomberg, D. M., Nissenbaum, H. · Under review, CHI '27
Autonomy by Design: Co-Designing a Framework to Evaluate Whether AI Interfaces Support User Autonomy
Sandhaus, H., Semler, J., Nissenbaum, H., Yang, Q. · Under review, CHI '27. Companion artifact: human-ai-autonomy-rubric.surge.sh
I Think We Did This: Erosion and Reassertion of Students' Design Intention in AI-Saturated Work
Sandhaus, H., Han, R., Parreira, M. T., Ju, W. · Under review, CHI '27
Values in Public Interest Tech: Practicing Conscientious Design
Sandhaus, H., Lee, J. J., Wang, A., Chiang, E., Gosciak, J., Liu, K., Greenstreet, L., Yessenbayev, O., Nissenbaum, H. · Revise and resubmit

Journal articles peer-reviewed

ZfP
Deception Is Ugly: Linking Aesthetic Judgment to Perceived Manipulation in Dark Patterns

Hauke Sandhaus, and Doris Maria Rhomberg

Zeitschrift für Psychologie, 2026

Accepted, forthcoming

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Dark patterns are manipulative interface designs that exploit cognitive biases. We surveyed 126 social media users who evaluated mockups of 15 dark patterns across six strategic categories, using an adapted User Experience Questionnaire with four autonomy-focused items (pressuring, addictive, covert, deceptive). Deception co-occurred strongly with negative aesthetic judgments (r = 0.93): users rated deceptive interfaces as unattractive, whereas pressure, addictiveness, and covertness showed weaker associations. Within strategic categories, Interface Interference (r = 0.77) and Social Engineering (r = 0.72) items cohered, indicating that several categories from existing taxonomies correspond to coherent user experiences. These exploratory results are consistent with users holding tacit awareness that becomes articulable when vocabulary is provided, with the four autonomy items functioning as distinct perceptual dimensions. They point to ethics-focused UX measurement scales as a direction for capturing unethical influence alongside pragmatic and hedonic constructs.
@article{Sandhaus2026-deception, title = {Deception Is Ugly: Linking Aesthetic Judgment to Perceived Manipulation in Dark Patterns}, author = {Sandhaus, Hauke and Rhomberg, Doris Maria}, year = {2026}, journal = {Zeitschrift für Psychologie}, publisher = {Hogrefe Publishing}, language = {en}, psyarxiv = {https://doi.org/10.31234/osf.io/bhfrz_v2}, thread = {ethical-design}, mentees = {doris-rhomberg}, pubtype = {journal}, note = {Accepted, forthcoming}, }
TLDR: Across 126 social media users, perceived deception co-occurs strongly with negative aesthetic judgments of dark patterns (r = 0.93), pointing toward ethics-focused UX measurement scales.
CSCW
My Precious Crash Data: Barriers and Opportunities in Encouraging Autonomous Driving Companies to Share Safety-Critical Data

Hauke Sandhaus, Angel Hsing-Chi Hwang, Wendy Ju, and 1 more author

Proceedings of the ACM on Human-Computer Interaction, Oct 2025

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Safety-critical data, such as crash and near-crash records, are crucial to improving autonomous vehicle (AV) design and development. Sharing such data across AV companies, academic researchers, regulators, and the public can help make all AVs safer. However, AV companies rarely share safety-critical data externally. This paper aims to pinpoint why AV companies are reluctant to share safety-critical data, with an eye on how these barriers can inform new approaches to promote sharing. We interviewed twelve AV company employees who actively work with such data in their day-to-day work. Findings suggest two key, previously unknown barriers to data sharing: (1) Datasets inherently embed salient knowledge that is key to improving AV safety and are resource-intensive. Therefore, data sharing, even within a company, is fraught with politics. (2) Interviewees believed AV safety knowledge is private knowledge that brings competitive edges to their companies, rather than public knowledge for social good. We discuss the implications of these findings for incentivizing and enabling safety-critical AV data sharing, specifically, implications for new approaches to (1) debating and stratifying public and private AV safety knowledge, (2) innovating data tools and data sharing pipelines that enable easier sharing of public AV safety data and knowledge; (3) offsetting costs of curating safety-critical data and incentivizing data sharing.
@article{Sandhaus2025-hc, title = {My Precious Crash Data: Barriers and Opportunities in Encouraging Autonomous Driving Companies to Share Safety-Critical Data}, author = {Sandhaus, Hauke and Hwang, Angel Hsing-Chi and Ju, Wendy and Yang, Qian}, year = {2025}, month = oct, journal = {Proceedings of the ACM on Human-Computer Interaction}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {9}, number = {7}, pages = {1--21}, articleno = {348}, doi = {10.1145/3757493}, url = {https://doi.org/10.1145/3757493}, issn = {2573-0142}, thread = {data-privacy}, pubtype = {journal}, }
TLDR: This study identifies barriers to sharing safety-critical data in autonomous vehicle companies, revealing that datasets are seen as competitive knowledge due to embedded salient knowledge, making sharing politically fraught.

Conference papers full papers, peer-reviewed

AIES
Context and Symmetry in Auditing: A Case Study of Skeleton Inference in Motion Capture

Emma Harvey, Emanuel Moss, Hauke Sandhaus, and 2 more authors

In Proceedings of the Ninth AAAI/ACM Conference on AI, Ethics, and Society (AIES ’26), Oct 2026

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AI systems that observe and make inferences about people should be checked through a contextual audit, which examines measurements within the practices that produce them. We show how this approach exposes assumptions in the audit process and makes explicit what serves as ground truth. We draw on the science and technology studies concept of symmetry to support audits when ground truth is unknown, unknowable, or contested, and close with a case study of skeleton inference in motion capture and directions for future audits of these systems.
@inproceedings{Harvey2026-aies, title = {Context and Symmetry in Auditing: A Case Study of Skeleton Inference in Motion Capture}, author = {Harvey, Emma and Moss, Emanuel and Sandhaus, Hauke and Jacobs, Abigail Z and Sloane, Mona}, year = {2026}, month = oct, booktitle = {Proceedings of the Ninth AAAI/ACM Conference on AI, Ethics, and Society (AIES '26)}, volume = {9}, number = {2}, pages = {1238--1251}, publisher = {AAAI Press}, doi = {10.1609/aies.v9i2.43220}, url = {https://doi.org/10.1609/aies.v9i2.43220}, eprint = {2608.10194}, archiveprefix = {arXiv}, primaryclass = {cs.CY}, keywords = {AI auditing, measurement, motion capture, ground truth, symmetry, science and technology studies}, thread = {data-privacy}, pubtype = {conference}, }
TLDR: Proposes the "contextual audit": checking an AI system’s measurements inside the practices that produce them, and borrowing symmetry from STS for cases where ground truth is unknown or contested. Case study on skeleton inference in motion capture.
FAccT
Privacy of Groups in Dense Street Imagery

Matt Franchi^*, Hauke Sandhaus^*, Madiha Zahrah Choksi, and 3 more authors

In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, Athens, Greece, Jun 2025

* = equal contribution

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Spatially and temporally dense street imagery (DSI) datasets have grown unbounded. In 2024, individual companies possessed around 3 trillion unique images of public streets. DSI data streams are only set to grow as companies like Lyft and Waymo use DSI to train autonomous vehicle algorithms and analyze collisions. Academic researchers leverage DSI to explore novel approaches to urban analysis. Despite good-faith efforts by DSI providers to protect individual privacy through blurring faces and license plates, these measures fail to address broader privacy concerns. In this work, we find that increased data density and advancements in artificial intelligence enable harmful group membership inferences from supposedly anonymized data. We perform a penetration test to demonstrate how easily sensitive group affiliations can be inferred from obfuscated pedestrians in 25,232,608 dashcam images taken in New York City. We develop a typology of identifiable groups within DSI and analyze privacy implications through the lens of contextual integrity. Finally, we discuss actionable recommendations for researchers working with data from DSI providers.
@inproceedings{franchi2025privacygroupsdensestreet, title = {Privacy of Groups in Dense Street Imagery}, author = {Franchi, Matt and Sandhaus, Hauke and Choksi, Madiha Zahrah and Engelmann, Severin and Ju, Wendy and Nissenbaum, Helen}, year = {2025}, doi = {10.1145/3715275.3732185}, isbn = {9798400714825}, url = {https://doi.org/10.1145/3715275.3732185}, booktitle = {Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency}, pages = {2874--2891}, numpages = {18}, keywords = {privacy, dense street imagery, group privacy, contextual integrity, computer vision, surveillance, penetration testing, auditing}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, series = {FAccT '25}, month = jun, location = {Athens, Greece}, note = {* = equal contribution}, thread = {data-privacy}, pubtype = {conference}, eprint = {2505.07085}, archiveprefix = {arXiv}, primaryclass = {cs.CY} }
TLDR: Dense street imagery enables harmful group membership inference despite individual anonymity. Contextual Integrity is used to analyze group based privacy implications based on appropriate information flows.
DIS
Co-Designing with Transformers: Unpacking the Complex Role of GenAI in Interactive System Design Education

Hauke Sandhaus, Qiuquan Gu, Maria Teresa Parreira, and 1 more author

In Proceedings of the 2025 ACM Designing Interactive Systems Conference, Funchal, Portugal, Jul 2025

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Generative Artificial Intelligence (GenAI) is transforming Human-Computer Interaction (HCI) education and technology design, yet its impact remains poorly understood. This study explores how graduate students in an applied HCI course used GenAI tools during interactive device design. Despite no encouragement, all groups integrated GenAI into their workflows. Through 12 post-class group interviews, we identified how GenAI co-design behaviors present both benefits, such as enhanced creativity and faster design iterations, and risks, including shallow learning and reflection. Benefits were most evident during the execution phases, while the discovery and reflection phases showed limited gains. A taxonomy of usage patterns revealed that students’ outcomes depended more on how they used GenAI than the specific tasks performed. These findings highlight the need for HCI education to adapt to GenAI’s role and offer recommendations for curricula to better prepare future designers for effective creative co-design.
@inproceedings{Sandhaus2025-jc, title = {Co-Designing with Transformers: Unpacking the Complex Role of GenAI in Interactive System Design Education}, author = {Sandhaus, Hauke and Gu, Qiuquan and Parreira, Maria Teresa and Ju, Wendy}, year = {2025}, month = jul, booktitle = {Proceedings of the 2025 ACM Designing Interactive Systems Conference}, location = {Funchal, Portugal}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, series = {DIS '25}, pages = {1228--1243}, doi = {10.1145/3715336.3735805}, isbn = {979-8-4007-1485-6/2025/07}, url = {https://doi.org/10.1145/3715336.3735805}, eprint = {2410.14048}, archiveprefix = {arXiv}, primaryclass = {cs.HC}, thread = {human-ai}, mentees = {qiuquan-gu}, pubtype = {conference}, }
TLDR: GenAI in Human-Computer Interaction education and design brings both benefits, such as enhanced creativity and faster iterations, and risks, including shallow learning and reflection. Students’ approach to GenAI, rather than the specific tasks performed, influences the success of GenAI co-design.
ITSC
Characterizing Cultural Differences in Naturalistic Driving Interactions

Rachel Di Pirro, Hauke Sandhaus, David Goedicke, and 3 more authors

In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), Sep 2024

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The characterization of driver interactions is important for a variety of problems associated with the design of autonomy for vehicles. We consider the role of cultural context in driver interactions, by evaluating the differences in driving interactions in simulated driving experiments conducted in New York City, New York, USA, and in Haifa, Israel. The same experiment was conducted in both locations, and focused on naturalistic driving interactions at unsigned intersections, in which interaction with another vehicle was required for safe navigation through the intersection. We employ conditional distribution embeddings, a nonparametric machine learning technique, to empirically characterize differences in the distribution of trajectories that characterize driver interactions, across both locations. We show that cultural variability outweighs individual variability in intersections that require turning maneuvers, and that clear distinctions amongst driving strategies are evident between populations. Our approach facilitates a data-driven analysis that is amenable to rigorous statistical testing, in a manner that minimizes filtering, pre-processing, and other manipulations that could inadvertently bias the data and obscure important findings.
@inproceedings{Di_Pirro_2024, title = {Characterizing Cultural Differences in Naturalistic Driving Interactions}, author = {Di Pirro, Rachel and Sandhaus, Hauke and Goedicke, David and Calderone, Dan and Oishi, Meeko and Ju, Wendy}, booktitle = {2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, pages = {3651--3658}, year = {2024}, month = sep, day = {24-27}, address = {Edmonton, Canada}, url = {https://its.papercept.net/conferences/scripts/abstract.pl?ConfID=87&Number=874}, doi = {10.1109/itsc58415.2024.10919603}, issn = {2153-0017,2153-0009}, language = {en}, thread = {automotive}, pubtype = {conference}, }
TLDR: This study compares driver interactions at unsigned intersections in New York City and Haifa, using MMD in Hilbert space, revealing that cultural differences significantly influence driving strategies, especially during turns.
AutoUI
Modeling Social Situation Awareness in Driving Interactions

Navit Klein, Hauke Sandhaus, David Goedicke, and 2 more authors

In Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Stanford, CA, USA, Sep 2024

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The design of self-driving vehicles requires an understanding of the social interactions between drivers in resolving vague encounters, such as at un-signalized intersections. In this paper, we make the case for social situation awareness as a model for understanding everyday driving interaction. Using a dual-participant VR driving simulator, we collected data from driving encounter scenarios to understand how (N=170) participant drivers behave with respect to one another. Using a social situation awareness questionnaire we developed, we assessed the participants’ social awareness of other driver’s direction of approach to the intersection, and also logged signaling, speed and speed change, and heading of the vehicle. Drawing upon the statistically significant relationships in the variables in the study data, we propose a Social Situation Awareness model based on the approach, speed, change of speed, heading and explicit signaling from drivers.
@inproceedings{10.1145/3640792.3675717, title = {Modeling Social Situation Awareness in Driving Interactions}, author = {Klein, Navit and Sandhaus, Hauke and Goedicke, David and Ju, Wendy and Parush, Avi}, year = {2024}, month = sep, booktitle = {Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, location = {Stanford, CA, USA}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, series = {AutomotiveUI '24}, pages = {259--271}, doi = {10.1145/3640792.3675717}, isbn = {9798400705106}, url = {https://doi.org/10.1145/3640792.3675717}, numpages = {13}, keywords = {Driving encounters, autonomous vehicles, social situational awareness (SA), unsignaled intersections, virtual reality (VR)}, thread = {automotive}, pubtype = {conference}, }
TLDR: Proposes a Social Situation Awareness model for better understanding driver negotiation at un-signalized intersections.
AutoUI
Changing Lanes Toward Open Science: Openness and Transparency in Automotive User Research

Patrick Ebel, Pavlo Bazilinskyy, Mark Colley, and 6 more authors

In Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Stanford, CA, USA, Sep 2024

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We review the state of open science and the perspectives on open data sharing within the automotive user research community. Openness and transparency are critical not only for judging the quality of empirical research, but also for accelerating scientific progress and promoting an inclusive scientific community. However, there is little documentation of these aspects within the automotive user research community. To address this, we report two studies that identify (1) community perspectives on motivators and barriers to data sharing, and (2) how openness and transparency have changed in papers published at AutomotiveUI over the past 5 years. We show that while open science is valued by the community and openness and transparency have improved, overall compliance is low. The most common barriers are legal constraints and confidentiality concerns. Although research published at AutomotiveUI relies more on quantitative methods than research published at CHI, openness and transparency are not as well established. Based on our findings, we provide suggestions for improving openness and transparency, arguing that the motivators for open science must outweigh the barriers. All supporting materials are freely available at: https://osf.io/zdpek/
@inproceedings{10.1145/3640792.3675730, title = {Changing Lanes Toward Open Science: Openness and Transparency in Automotive User Research}, author = {Ebel, Patrick and Bazilinskyy, Pavlo and Colley, Mark and Goodridge, Courtney Michael and Hock, Philipp and Janssen, Christian P. and Sandhaus, Hauke and Srinivasan, Aravinda Ramakrishnan and Wintersberger, Philipp}, year = {2024}, month = sep, booktitle = {Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, location = {Stanford, CA, USA}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, series = {AutomotiveUI '24}, pages = {94--105}, doi = {10.1145/3640792.3675730}, isbn = {9798400705106}, url = {https://doi.org/10.1145/3640792.3675730}, numpages = {12}, keywords = {AutomotiveUI, open data, open science, openness, reproducibility, transparency}, thread = {automotive}, pubtype = {conference}, }
TLDR: Analyzes how openness and transparency have evolved in automotive user research and suggests steps for improving data sharing.
CHI
The Cadaver in the Machine: The Social Practices of Measurement and Validation in Motion Capture Technology

Emma Harvey, Hauke Sandhaus, Abigail Z Jacobs, and 2 more authors

Proceedings of the CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, May 2024

Honorable Mention Abs arXiv Bib HTML

Best Paper Honorable Mention at CHI ’24

Motion capture systems, used across various domains, make body representations concrete through technical processes. We argue that the measurement of bodies and the validation of measurements for motion capture systems can be understood as social practices. By analyzing the findings of a systematic literature review (N=278) through the lens of social practice theory, we show how these practices, and their varying attention to errors, become ingrained in motion capture design and innovation over time. Moreover, we show how contemporary motion capture systems perpetuate assumptions about human bodies and their movements. We suggest that social practices of measurement and validation are ubiquitous in the development of data- and sensor-driven systems more broadly, and provide this work as a basis for investigating hidden design assumptions and their potential negative consequences in human-computer interaction.
@article{Harvey2024-gn, title = {The Cadaver in the Machine: The Social Practices of Measurement and Validation in Motion Capture Technology}, author = {Harvey, Emma and Sandhaus, Hauke and Jacobs, Abigail Z and Moss, Emanuel and Sloane, Mona}, year = {2024}, month = may, journal = {Proceedings of the CHI Conference on Human Factors in Computing Systems}, archiveprefix = {arXiv}, primaryclass = {cs.CY}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, articleno = {768}, numpages = {23}, keywords = {anthropometry, measurement, motion capture, social practices, validation}, location = {Honolulu, HI, USA}, series = {CHI '24}, doi = {10.1145/3613904.3642004}, url = {https://doi.org/10.1145/3613904.3642004}, howpublished = {CHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems}, thread = {data-privacy}, pubtype = {conference}, }
TLDR: Motion capture systems are built and validated on old assumptions as a literature review using social practice theory shows.
D TEX
Smart Textiles in the Performing Arts

Aline Martinez, Michaela Honauer, Hauke Sandhaus, and 1 more author

Textiles, Identity and Innovation: Design the Future, May 2018

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We present the Sonification Costume, an interactive costume created for modern dance performances, that makes movements perceivable through sound. We created it in the context of studying textile-based sensors, and here describe the exploration of different production techniques, which led to a knitted whole-body suit with seamlessly integrated stretch sensors. Self-reflection and a user study reveal that the costume concept is interesting to dancers and how we can further improve textile-integrated sensors.
@article{Martinez2018-xc, title = {Smart Textiles in the Performing Arts}, author = {Martinez, Aline and Honauer, Michaela and Sandhaus, Hauke and Hornecker, Eva}, year = {2018}, journal = {Textiles, Identity and Innovation: Design the Future}, publisher = {Taylor & Francis}, url = {https://www.taylorfrancis.com/chapters/edit/10.1201/9781315100210-57/smart-textiles-performing-arts-aline-martinez-michaela-honauer-hauke-sandhaus-eva-hornecker}, pubtype = {conference}, }
TLDR: Our dance costume using smart textiles makes movements perceivable through sound.

Short papers, posters & late-breaking work peer-reviewed

CHIWORK LBW
Making Indecent Persuasion Visible: How Evaluation Metrics Shape UX Designers’ Ethical Reasoning

Hauke Sandhaus, Doris Maria Rhomberg, and Helen Nissenbaum

In CHIWORK ’26: Proceedings of the 5th Annual Symposium on Human-Computer Interaction for Work (Late-Breaking Work), Linz, Austria, Jun 2026

Best LBW Award Abs Bib HTML PDF Poster

Best Late-Breaking Work Award at CHIWORK ’26 (1 of 25)

UX evaluation tools are widely treated as neutral measurement instruments, yet the metrics designers see during evaluation may determine what they can recognise as ethically problematic. In a three-condition experiment, 141 UX professionals evaluated social media interfaces containing dark patterns. When dashboards included persuasion-focused items alongside standard User Experience Questionnaire (UEQ) data, designers rejected manipulative interfaces at nearly double the rate of a no-data baseline (56.2% vs. 30.0%). Standard UEQ data alone already produced a significant intermediate shift (43.9% rejection), so any user-testing signal recalibrates judgement. Decision confidence stayed uniformly high across conditions: designers without evaluation data were not cautiously uncertain but confidently uncalibrated, reaching different conclusions than they would have with user data while feeling equally sure. Analysis of 1,313 written justifications showed that persuasion-focused data shifted reasoning from business and aesthetic framings toward explicit engagement with manipulation, user trust, and autonomy. We introduce indecent persuasion, behavioural influence that users experience as pressuring, addictive, covert, or deceptive, as a dimension of user experience that standard instruments fail to surface. The findings raise a question about evaluation infrastructure: who decides which dimensions of user experience count?
@inproceedings{Sandhaus2026-indecent, title = {Making Indecent Persuasion Visible: How Evaluation Metrics Shape UX Designers' Ethical Reasoning}, author = {Sandhaus, Hauke and Rhomberg, Doris Maria and Nissenbaum, Helen}, year = {2026}, month = jun, booktitle = {CHIWORK '26: Proceedings of the 5th Annual Symposium on Human-Computer Interaction for Work (Late-Breaking Work)}, location = {Linz, Austria}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, series = {CHIWORK '26}, doi = {10.1145/3805029.3818279}, url = {https://doi.org/10.1145/3805029.3818279}, keywords = {UX metrics, dark patterns, indecent persuasion, ethical design, evaluation instruments, professional judgment}, thread = {ethical-design}, mentees = {doris-rhomberg}, pubtype = {short}, }
TLDR: In a study of 141 UX professionals, persuasion-focused evaluation metrics nearly doubled rejection of manipulative interfaces, surfacing "indecent persuasion" as a dimension standard UX instruments fail to capture.
AutoUI WiP
Regaining Trust: Impact of Transparent User Interface Design on Acceptance of Camera-Based In-Car Health Monitoring Systems

Hauke Sandhaus, Madiha Zahrah Choksi, and Wendy Ju

In Adjunct Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Stanford, CA, USA, Sep 2024

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Introducing in-car health monitoring systems offers substantial potential to improve driver safety. However, camera-based sensing technologies introduce significant privacy concerns. This study investigates the impact of transparent user interface design on user acceptance of these systems. We conducted an online study with 42 participants using prototypes varying in transparency, choice, and deception levels. The prototypes included three onboarding designs: (1) a traditional Terms and Conditions text, (2) a Business Nudge design that subtly encouraged users to accept default data-sharing options, and (3) a Transparent Walk-Through that provided clear, step-by-step explanations of data use and privacy policies. Our findings indicate that transparent design significantly affects user experience measures, including perceived creepiness, trust in data use, and trustworthiness of content. Transparent onboarding processes enhanced user experience and trust without significantly increasing onboarding time. These findings offer practical guidance for designing user-friendly and privacy-respecting in-car health monitoring systems.
@inproceedings{sandhaus2024regainingtrustimpacttransparent, title = {Regaining Trust: Impact of Transparent User Interface Design on Acceptance of Camera-Based In-Car Health Monitoring Systems}, author = {Sandhaus, Hauke and Choksi, Madiha Zahrah and Ju, Wendy}, year = {2024}, month = sep, booktitle = {Adjunct Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications}, location = {Stanford, CA, USA}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, series = {AutomotiveUI '24 Adjunct}, pages = {203--208}, doi = {10.1145/3641308.3685048}, isbn = {9798400705205}, url = {https://doi.org/10.1145/3641308.3685048}, numpages = {6}, keywords = {Creepiness, In-Car Health Monitoring, Online Study, Pervasive Sensing, Privacy, Technology Acceptance, Transparent Design, Trustworthiness, User Experience, Vehicle}, archiveprefix = {arXiv}, primaryclass = {cs.HC}, thread = {data-privacy}, pubtype = {short}, }
TLDR: Transparently designed privacy onboarding can enhance user trust in camera-based in-car health monitoring systems.

AutoUI WiP

Exploring Interactions for Collaborative Driving in Fully Automated Vehicles

Tamara Sawitzky, Ina Marie Koniakowsky, Qidan Yan, and 2 more authors

In Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Ingolstadt, Germany, Sep 2023

Bib HTML

@inproceedings{vonSawitzky2023-collab,
  title = {Exploring Interactions for Collaborative Driving in Fully Automated Vehicles},
  author = {von Sawitzky, Tamara and Koniakowsky, Ina Marie and Yan, Qidan and Sandhaus, Hauke and Askari, Ali},
  year = {2023},
  month = sep,
  booktitle = {Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {AutomotiveUI '23 Adjunct},
  location = {Ingolstadt, Germany},
  pages = {340--341},
  doi = {10.1145/3581961.3609851},
  url = {https://doi.org/10.1145/3581961.3609851},
  keywords = {automated vehicles, collaborative driving, prototyping workshop},
  pubtype = {short},
}

TLDR: Prototyping-workshop outcome: how passengers could still shape the driving of a fully automated vehicle once steering wheel and pedals are gone.

CSCW
VR Job Interview Using a Gender-Swapped Avatar

Jieun Kim^*, Hauke Sandhaus^*, and Susan R Fussell

In Computer Supported Cooperative Work and Social Computing, Oct 2023

* = equal contribution

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Virtual Reality (VR) has emerged as a potential solution for mitigating bias in a job interview by hiding the applicants’ demographic features. The current study examines the use of a gender-swapped avatar in a virtual job interview that affects the applicants’ perceptions and their performance evaluated by recruiters. With a mixed-method approach, we first conducted a lab experiment (N=8) exploring how using a gender-swapped avatar in a virtual job interview impacts perceived anxiety, confidence, competence, and ability to perform. Then, a semi-structured interview investigated the participants’ VR interview experiences using an avatar. Our findings suggest that using gender-swapped avatars may reduce the anxiety that job applicants will experience during the interview. Also, the affinity diagram produced seven key themes highlighting the advantages and limitations of VR as an interview platform. These findings contribute to the emerging field of VR-based recruitment and have practical implications for promoting diversity and inclusion in the hiring process.
@inproceedings{Kim2023-no, title = {VR Job Interview Using a Gender-Swapped Avatar}, author = {Kim, Jieun and Sandhaus, Hauke and Fussell, Susan R}, year = {2023}, month = oct, booktitle = {Computer Supported Cooperative Work and Social Computing}, publisher = {ACM}, address = {New York, NY, USA}, doi = {10.1145/3584931.3606976}, url = {https://doi.org/10.1145/3584931.3606976}, language = {en}, eprint = {2307.04247}, archiveprefix = {arXiv}, primaryclass = {cs.HC}, howpublished = {CSCW '23 Companion}, pubtype = {short}, note = {* = equal contribution}, }
TLDR: Gender-swapped avatars in virtual job interviews influences applicant’s perception of performance.
UIST
A WOZ Study of Feedforward Information on an Ambient Display in Autonomous Cars

Hauke Sandhaus, and Eva Hornecker

In The 31st Annual ACM Symposium on User Interface Software and Technology Adjunct Proceedings, Berlin, Germany, Oct 2018

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We describe the development and user testing of an ambient display for autonomous vehicles. Instead of providing feedback about driving actions, once executed, it communicates driving decisions in advance, via light signals in passengers’ peripheral vision. This ambient display was tested in a WoZ-based on-the-road-driving simulation of a fully autonomous vehicle. Findings from a preliminary study with 14 participants suggest that such a display might be particularly useful to communicate upcoming inertia changes for passengers.
@inproceedings{Sandhaus2018-el, title = {A WOZ Study of Feedforward Information on an Ambient Display in Autonomous Cars}, author = {Sandhaus, Hauke and Hornecker, Eva}, year = {2018}, month = oct, booktitle = {The 31st Annual {ACM} Symposium on User Interface Software and Technology Adjunct Proceedings}, location = {Berlin, Germany}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, series = {UIST '18 Adjunct}, pages = {90--92}, doi = {10.1145/3266037.3266111}, isbn = {9781450359498}, url = {https://doi.org/10.1145/3266037.3266111}, keywords = {on-road simulation, autonomous vehicle interfaces, methodology, ambient display}, pubtype = {short}, }
TLDR: Ambient displays in autonomous vehicles can subtly communicate driving decisions to passengers (Master Thesis).

Workshop papers

CHIWORK Workshop
Visible Adoption, Untracked Contribution: GitHub Evidence of the Accountability Gap Across Three Cohorts of an HCI Prototyping Course

Maria Teresa Parreira, Pranav Prabhat Sinha, Hauke Sandhaus, and 1 more author

In CHIWORK ’26: Adjunct Proceedings of the 5th Annual Symposium on Human-Computer Interaction for Work, Linz, Austria, Jun 2026

Workshop paper at CHIWORK ’26 (workshop I co-organized)

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This paper presents a longitudinal, observational case study of how student GenAI adoption shifted across three cohorts (Fall 2022, 2023, and 2025) of the same graduate-level HCI prototyping course, using computational analysis of 203 GitHub repositories with student activity and 23,065 student commits. Building on a prior qualitative study of the 2023 cohort, we distinguish two levels of AI accountability trace: disclosure (naming that an AI tool was used) and attribution (crediting a specific artifact or task to an AI tool). We find that tool disclosure grew from 0% to 66% of repositories across the three cohorts, while explicit contribution attribution remains a minority practice, and the gap between the two reveals where accountability is missing even among students who disclose. By 2025, AI is infrastructure embedded in course templates and student-built devices: students increasingly name the tools they used, but rarely specify what those tools contributed. We argue that disclosure-based frameworks are insufficient for the vibe-coding era. The failure is not that students conceal AI use; it is that a norm built for episodic, identifiable acts cannot capture continuous, ambient co-creation. We offer this case study as grounding for the workshop’s conversation about what genuine co-thinking accountability looks like.
@inproceedings{Parreira2026-visible, title = {Visible Adoption, Untracked Contribution: GitHub Evidence of the Accountability Gap Across Three Cohorts of an HCI Prototyping Course}, author = {Parreira, Maria Teresa and Sinha, Pranav Prabhat and Sandhaus, Hauke and Ju, Wendy}, year = {2026}, month = jun, booktitle = {CHIWORK '26: Adjunct Proceedings of the 5th Annual Symposium on Human-Computer Interaction for Work}, location = {Linz, Austria}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, series = {CHIWORK '26 Adjunct}, eprint = {2606.14054}, archiveprefix = {arXiv}, primaryclass = {cs.HC}, mentees = {pranav-sinha}, pubtype = {workshop}, note = {Workshop paper at CHIWORK '26 (workshop I co-organized)}, }
TLDR: Analysis of 203 GitHub repos across three HCI-course cohorts shows disclosure of AI-tool use grew to 66%, but explicit contribution attribution stayed rare — revealing an accountability gap in the vibe-coding era.
CHI Workshop
Student Reflections on Self-Initiated GenAI Use in HCI Education

Hauke Sandhaus, Maria Teresa Parreira, and Wendy Ju

In LLMs as Research Tools Workshop at the CHI Conference on Human Factors in Computing Systems, Honolulu, Hawaii, USA, May 2024

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This study explores students’ self-initiated use of Generative Artificial Intelligence (GenAI) tools in an interactive systems design class. Through 12 group interviews, students revealed the dual nature of GenAI in (1) stimulating creativity and (2) speeding up design iterations, alongside concerns over its potential to cause shallow learning and reliance. GenAI’s benefits were pronounced in the execution phase of design, aiding rapid prototyping and ideation, while its use in initial insight generation posed risks to depth and reflective practice. This reflection highlights the complex role of GenAI in Human-Computer Interaction education, emphasizing the need for balanced integration to leverage its advantages without compromising fundamental learning outcomes.
@inproceedings{sandhaus_student_2024, title = {Student Reflections on Self-Initiated GenAI Use in HCI Education}, author = {Sandhaus, Hauke and Parreira, Maria Teresa and Ju, Wendy}, year = {2024}, month = may, booktitle = {LLMs as Research Tools Workshop at the CHI Conference on Human Factors in Computing Systems}, location = {Honolulu, Hawaii, USA}, language = {en}, primaryclass = {cs.HC}, archiveprefix = {arXiv}, pubtype = {workshop}, url = {https://arxiv.org/abs/2405.01467}, eprint = {2405.01467}, }
TLDR: Generative AI tools in HCI design can accelerate iterations, but also raise concerns among students about shallow learning.
CHI Workshop
Towards Quantifying Ethical User Experience: Evaluating User Perceptions of Dark Patterns in Social Media

Doris Maria Rhomberg^*, and Hauke Sandhaus^*

In Mobilizing Research and Regulatory Action on Dark Patterns and Deceptive Design Practices Workshop at the CHI Conference on Human Factors in Computing Systems, Honolulu, Hawaii, USA, May 2024

* = equal contribution

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No standardized questionnaire currently incorporates an ethical dimension for assessing User Experience (UX). We explored how the ethicality of interface design is reflected in current UX metrics and how they could be extended. To this end, we adapted the User Experience Questionnaire (UEQ) and enriched it with supplementary items specifically designed to capture user responses on social media to unethical interface designs, commonly referred to as ’dark patterns’. Through an exploratory analysis of a survey involving 120 participants who evaluated a selection of 15 social media dark patterns, we found preliminary evidence that (1) an aggregated UX score using items from the User Experience Questionnaire does not effectively indicate unethical user interface design. Instead, (2) subscale measures from the questionnaire show a relationship with unethical design. Furthermore, extending the User Experience Questionnaire seems promising, as (3) users can identify interfaces with addictive and pressuring properties, and (4) evaluations demonstrate consistency within groups of unethical design strategies.
@inproceedings{rhomberg_towards_2024, title = {Towards Quantifying Ethical User Experience: Evaluating User Perceptions of Dark Patterns in Social Media}, author = {Rhomberg, Doris Maria and Sandhaus, Hauke}, year = {2024}, month = may, booktitle = {Mobilizing Research and Regulatory Action on Dark Patterns and Deceptive Design Practices Workshop at the CHI Conference on Human Factors in Computing Systems}, location = {Honolulu, Hawaii, USA}, language = {en}, mentees = {doris-rhomberg}, pubtype = {workshop}, note = {* = equal contribution}, }
TLDR: Only a modified User Experience Questionnaire can explicate unethical aspects of social media interfaces.
CHI Workshop
Promoting Bright Patterns

Hauke Sandhaus

In CHI ’23 Workshop: Designing Technology and Policy Simultaneously, Hamburg, Germany, Apr 2023

This paper started the brightpatterns.org website

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User experience designers are facing increasing scrutiny and criticism for creating harmful technologies, leading to a pushback against unethical design practices. While clear-cut harmful practices such as dark patterns have received attention, trends towards automation, personalization, and recommendation present more ambiguous ethical challenges. To address potential harm in these "gray" instances, we propose the concept of "bright patterns" - persuasive design solutions that prioritize user goals and well-being over their desires and business objectives. The ambition of this paper is threefold: to define the term "bright patterns", to provide examples of such patterns, and to advocate for the adoption of bright patterns through policymaking.
@inproceedings{sandhaus2023promoting, title = {Promoting Bright Patterns}, author = {Sandhaus, Hauke}, year = {2023}, booktitle = {CHI '23 Workshop: Designing Technology and Policy Simultaneously}, eprint = {2304.01157}, archiveprefix = {arXiv}, primaryclass = {cs.HC}, thread = {ethical-design}, note = {This paper started the <a href="https://brightpatterns.org/" target="_blank">brightpatterns.org</a> website}, pubtype = {workshop}, location = {Hamburg, Germany}, month = apr, day = {23} }
TLDR: The first definition of "bright patterns" is accompanied by examples that illustrate corporate use of persuasive design in support of user goals over their desires and business objectives.
CHI Workshop
Towards Prototyping Driverless Vehicle Behaviors, City Design, and Policies Simultaneously

Hauke Sandhaus, Wendy Ju, and Qian Yang

In CHI ’23 Workshop: Designing Technology and Policy Simultaneously, Hamburg, Germany, Apr 2023

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Autonomous Vehicles (AVs) can potentially improve urban living by reducing accidents, increasing transportation accessibility and equity, and decreasing emissions. Realizing these promises requires the innovations of AV driving behaviors, city plans and infrastructure, and traffic and transportation policies to join forces. However, the complex interdependencies among AV, city, and policy design issues can hinder their innovation. We argue the path towards better AV cities is not a process of matching city designs and policies with AVs’ technological innovations, but a process of iterative prototyping of all three simultaneously: Innovations can happen step-wise as the knot of AV, city, and policy design loosens and tightens, unwinds and reties. In this paper, we ask: How can innovators innovate AVs, city environments, and policies simultaneously and productively toward better AV cities? The paper has two parts. First, we map out the interconnections among the many AV, city, and policy design decisions, based on a literature review spanning HCI/HRI, transportation science, urban studies, law and policy, operations research, economy, and philosophy. This map can help innovators identify design constraints and opportunities across the traditional AV/city/policy design disciplinary bounds. Second, we review the respective methods for AV, city, and policy design, and identify key barriers in combining them: (1) Organizational barriers to AV-city-policy design collaboration, (2) computational barriers to multi-granularity AV-city-policy simulation, and (3) different assumptions and goals in joint AV-city-policy optimization. We discuss two broad approaches that can potentially address these challenges, namely, "low-fidelity integrative City-AV-Policy Simulation (iCAPS)" and "participatory design optimization".
@inproceedings{sandhaus2023prototyping, title = {Towards Prototyping Driverless Vehicle Behaviors, City Design, and Policies Simultaneously}, author = {Sandhaus, Hauke and Ju, Wendy and Yang, Qian}, year = {2023}, booktitle = {CHI '23 Workshop: Designing Technology and Policy Simultaneously}, eprint = {2304.06639}, archiveprefix = {arXiv}, primaryclass = {cs.HC}, thread = {automotive}, pubtype = {workshop}, location = {Hamburg, Germany}, month = apr, day = {23} }
TLDR: Maps out the interdependencies between AV, city, and policy design and discusses methods for iteratively prototyping all three simultaneously.

Workshops organized as co-organizer

CHIWORK
Interrogating GenAI Augmentation for CHIworkers: Strategies for Professional Autonomy and Accountability

Hauke Sandhaus, Kashif Imteyaz, Mohammed Almutairi, and 5 more authors

In CHIWORK ’26: Proceedings of the 5th Annual Symposium on Human-Computer Interaction for Work (Workshop), Linz, Austria, Jun 2026

Workshop co-organizer

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As Generative AI (GenAI) becomes deeply embedded in UX research, design, and software engineering, the HCI community faces a pressing challenge: balancing the acceleration of output with the risks of de-skilling, loss of flow state, and diminished accountability. The goal of this workshop is to move beyond simple AI disclosure statements to figure out how HCI professionals can maintain "deep work" and intellectual autonomy, rather than merely "scraping by" using AI outputs, and when full hand-off may be appropriate. Participants will be invited to bring concrete examples of AI-augmented workflows they consider responsible, or irresponsible, drawn from active practice. Through mapping real-world AI use and collaboratively brainstorming countermeasures, this hybrid workshop will bring together researchers and practitioners to define what ethical "co-thinking, co-creating and co-augmenting with AI" looks like. Ultimately, we aim to produce a shared repository of evolving responsible practices or a professional code of conduct, helping the community transition toward accountable, professional GenAI-augmented workflows.
@inproceedings{Sandhaus2026-chiwork-ai, title = {Interrogating GenAI Augmentation for CHIworkers: Strategies for Professional Autonomy and Accountability}, author = {Sandhaus, Hauke and Imteyaz, Kashif and Almutairi, Mohammed and Prajod, Pooja and Ramesh, Divya and Savage, Saiph and Yang, Qian and Muller, Michael}, year = {2026}, month = jun, booktitle = {CHIWORK '26: Proceedings of the 5th Annual Symposium on Human-Computer Interaction for Work (Workshop)}, location = {Linz, Austria}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, series = {CHIWORK '26}, doi = {10.1145/3805029.3818271}, url = {https://doi.org/10.1145/3805029.3818271}, keywords = {Generative AI, Professional Ethics, Future of Work, HCI Practice, Accountability, Co-thinking}, thread = {human-ai}, pubtype = {organized}, note = {Workshop co-organizer}, }
TLDR: A CHIWORK 2026 workshop moving beyond AI disclosure statements to ask how HCI professionals can preserve deep work, intellectual autonomy, and accountability in GenAI-augmented workflows.

CHI Workshop

Bridge Over Troubled Water: Aligning Commercial Incentives With Ethical Design Practice To Combat Deceptive Patterns

Thomas Eßmeyer, Johanna Gunawan, Colin M Gray, and 8 more authors

In Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems, Apr 2026

Workshop co-organizer

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@inproceedings{Essmeyer2026-oa,
  title = {Bridge Over Troubled Water: Aligning Commercial Incentives With Ethical Design Practice To Combat Deceptive Patterns},
  author = {Eßmeyer, Thomas and Gunawan, Johanna and Gray, Colin M and Sánchez Chamorro, Lorena and Monge Roffarello, Alberto and Seaborn, Katie and Sandhaus, Hauke and Lukoff, Kai and Savino, Gian-Luca and Chivukula, Shruthi Sai and Nakamura, Satoshi},
  year = {2026},
  month = apr,
  doi = {10.1145/3772363.3778758},
  url = {https://doi.org/10.1145/3772363.3778758},
  booktitle = {Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {CHI EA '26},
  keywords = {Dark Patterns, Ethics and Values, Legal and Policy Perspectives, Regulation, UX Practice},
  pubtype = {organized},
  note = {Workshop co-organizer},
}

TLDR: A CHI 2026 workshop bringing researchers and practitioners together to align commercial incentives with ethical design practice and combat deceptive patterns.

CSCW Workshop
“Nobody Did This”: Contribution, Originality, and Accountability in Agent-Mediated Collaboration

Kashif Imteyaz, Mohammad Rashidujjaman Rifat, Divya Ramesh, and 6 more authors

In Proceedings of the 2026 Companion of the Computer-Supported Cooperative Work and Social Computing, Salt Lake City, USA, Nov 2026

Workshop co-organizer

Abs Bib Website

Collaborative knowledge work is changing in ways that go beyond disclosure or transparency. LLM agents are now embedded in how teams research, design, write, and decide: mediating between members, synthesizing inputs, reformulating ideas, and drafting shared outputs. They do not only facilitate collaboration; they operate within the workflow at the moment contributions are being formed. In doing so, they risk undermining the social conditions under which contribution can be witnessed, attributed, and held accountable. This workshop brings together researchers and practitioners to confront what we call contribution dissolution: the blurring of attribution, originality, and accountability in agent-mediated collaborative work. We argue that this dissolution begins before collaboration itself, in the individual worker’s own uncertainty about what is genuinely theirs, and propagates through collaborative relationships, collapsing the reliability that makes productive intellectual exchange possible. Through position statements, mapping exercises, and a hands-on activity, participants will surface how framing accountability as a documentation problem (e.g., AI use statements, watermarking, provenance logs) overlooks the conditions under which accountability is produced. Our goal is to produce a shared research agenda and the foundations of an infrastructural response to contribution dissolution in collaborative knowledge work.
@inproceedings{10.1145/3785651.3818822, title = {``Nobody Did This'': Contribution, Originality, and Accountability in Agent-Mediated Collaboration}, author = {Imteyaz, Kashif and Rifat, Mohammad Rashidujjaman and Ramesh, Divya and Rick, Steven R and Hosio, Simo and Sandhaus, Hauke and Sarkar, Advait and Riedl, Christoph and Savage, Saiph}, year = {2026}, month = nov, isbn = {9798400723780}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3785651.3818822}, doi = {10.1145/3785651.3818822}, booktitle = {Proceedings of the 2026 Companion of the Computer-Supported Cooperative Work and Social Computing}, pages = {723--727}, numpages = {5}, keywords = {Generative AI, Collaboration, Future of Work, Co-thinking and Co-Creation}, location = {Salt Lake City, USA}, series = {CSCW Companion '26}, pubtype = {organized}, note = {Workshop co-organizer}, }
TLDR: A CSCW 2026 workshop confronting "contribution dissolution": the blurring of attribution, originality, and accountability when LLM agents mediate collaborative knowledge work.

CHI Workshop

Mobilizing Research and Regulatory Action on Dark Patterns and Deceptive Design Practices

Colin M Gray, Johanna T Gunawan, René Schäfer, and 5 more authors

In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, May 2024

Workshop co-organizer; co-editor of the workshop proceedings, CEUR-WS Vol. 3720

Bib HTML Website

@inproceedings{Gray2024-ddp,
  title = {Mobilizing Research and Regulatory Action on Dark Patterns and Deceptive Design Practices},
  author = {Gray, Colin M and Gunawan, Johanna T and Schäfer, René and Bielova, Nataliia and Sánchez Chamorro, Lorena and Seaborn, Katie and Mildner, Thomas and Sandhaus, Hauke},
  year = {2024},
  month = may,
  booktitle = {Extended Abstracts of the CHI Conference on Human Factors in Computing Systems},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {CHI EA '24},
  location = {Honolulu, HI, USA},
  doi = {10.1145/3613905.3636310},
  url = {https://doi.org/10.1145/3613905.3636310},
  keywords = {dark patterns, deceptive design, regulation, policy, HCI},
  pubtype = {organized},
  note = {Workshop co-organizer; co-editor of the workshop proceedings, CEUR-WS Vol. 3720},
}

TLDR: A CHI 2024 workshop connecting dark-patterns scholarship to the legal, design, and regulatory communities; proceedings published as CEUR-WS Vol. 3720.

AutoUI Workshop

Breaking Barriers: Workshop on Open Data Practices in AutoUI Research

Patrick Ebel, Pavlo Bazilinskyy, Angel Hsing-Chi Hwang, and 5 more authors

In Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Ingolstadt, Germany, Sep 2023

Workshop co-organizer

Bib HTML

@inproceedings{Ebel2023-breaking,
  title = {Breaking Barriers: Workshop on Open Data Practices in AutoUI Research},
  author = {Ebel, Patrick and Bazilinskyy, Pavlo and Hwang, Angel Hsing-Chi and Ju, Wendy and Sandhaus, Hauke and Srinivasan, Aravinda Ramakrishnan and Yang, Qian and Wintersberger, Philipp},
  year = {2023},
  month = sep,
  booktitle = {Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {AutomotiveUI '23 Adjunct},
  location = {Ingolstadt, Germany},
  pages = {227--230},
  doi = {10.1145/3581961.3609835},
  url = {https://doi.org/10.1145/3581961.3609835},
  keywords = {open data, open science, automotive user research},
  pubtype = {organized},
  note = {Workshop co-organizer},
}

TLDR: Workshop on why experimental data sharing is still rare in AutoUI research and what best practices and open data formats could change that. Preceded the AutoUI ’24 "Changing Lanes" paper.

Other thesis, non-archival

Poster
The Privacy Policy Analysis Canvas: An Ethics Thinking Activity for Teaching Privacy Policy Insufficiency

Hauke Sandhaus, and Helen Nissenbaum

Oct 2025

Non-archival poster; write-up unpublished

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Privacy policies are the primary method used by digital service providers to inform users about their privacy practices. The privacy policy is also the mechanism employed by those providers to comply with the regulatory demand of public notice. Research has shown that users typically do not read privacy policies and often struggle to understand them due to their length, vagueness, complicated language, and incompleteness. To encourage deeper engagement with complex privacy practices, we developed a structured privacy policy analysis canvas for educational purposes. This allows students to critically examine and reflect on corporate privacy practices while revealing the fundamental insufficiency of notice-based approaches for privacy protection. We tested this canvas in a privacy class with 59 students who reviewed 31 privacy policies from diverse digital application domains. We report insights from students’ use of the canvas and make it available for wide adoption as a customizable design template.
@misc{Sandhaus2025-canvas, title = {The Privacy Policy Analysis Canvas: An Ethics Thinking Activity for Teaching Privacy Policy Insufficiency}, author = {Sandhaus, Hauke and Nissenbaum, Helen}, year = {2025}, month = oct, howpublished = {Poster at NYC Privacy Day 2025, Cornell Tech, New York, NY}, note = {Non-archival poster; write-up unpublished}, keywords = {privacy policy, contextual integrity, privacy education, analysis canvas}, pubtype = {other}, }
TLDR: A FigJam canvas that walks students through a privacy policy with contextual integrity: 59 students analyzed 31 policies in one class period, and the exercise shows why notice alone cannot protect privacy. Template translated into nine languages.
UT
Development of a 3D Navigable Interface for a Touchless Showcase

Hauke Sandhaus

Oct 2015

Bachelor’s thesis, University of Twente

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Due to widespread usage of touchscreens, users have accepted interaction without haptic feedback. Commercial game consoles have introduced users to touchless input and natural user interfaces. Only recently it has become technically possible to track human hand skeleton in high detail, precision, and low latency contactless. This report describes the design process for the user experience, user interface, and interaction interface of a touchless showcase using skeletal hand tracking. The project extends on existing hardware, compares current approaches, researches on the user group, and rebuilds the software, based on these findings, from scratch. To allow input, multiple interactions and 3D buttons were developed. In a user experiment, these were tested. A final prototype was implemented on two showcases for a month and compared to the existing software. The findings suggest that users want more natural and direct interactions. Missing physical feedback, non-existent depth perception on traditional screens, and unfamiliar gesture language are hindrances but can be overcome with the use of good visual cues. Future work on the software is solely on refinement. Some of the interactions investigated in this thesis will prevail, while some might appear in other contexts.
@misc{Sandhaus2015-in, title = {Development of a {3D} Navigable Interface for a Touchless Showcase}, author = {Sandhaus, Hauke}, year = {2015}, note = {Bachelor's thesis, University of Twente}, howpublished = {Bachelor's thesis, University of Twente}, pubtype = {other}, }
TLDR: An intuitive 3D navigable interface for a touchless showcase using skeletal hand tracking (Bachelor Thesis).