Hauke G.W. Sandhaus

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 political and fraught. (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.

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.

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/

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.

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.

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.

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.

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.

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.

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".