Hauke G.W. Sandhaus

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.

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?

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.

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.

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.

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.

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.

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.

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.

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.

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.

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/