Carina Manger

To develop truly human-centered automated systems, it is essential to acknowledge that human reasoning is prone to systematic deviations from rational judgment, known as Cognitive Biases. The present study investigated such flawed reasoning in the context of automated driving. In a multi-step study with N = 34 participants, the occurrence of four Cognitive Biases was examined: Truthiness Effect, Automation Bias, Action Bias, and Illusory Control. Additionally, the study explored how the Explainability of the automation’s behavior and the driver’s Mental Model influenced the manifestation of these biases. The findings indicate a notable susceptibility to the Truthiness Effect and Illusory Control, although all biases appeared highly dependent on the specific driving context. Moreover, Explainability strongly impacted the perceived credibility of information and participants’ agreement with the system’s behavior. Given the exploratory nature of the study, this work aims to initiate a discussion on how Cognitive Biases shape human reasoning and decision-making in interactions with automated vehicles. Based on the results, several directions for future research are proposed: (1) investigation of additional cognitive biases, (2) analysis of biases across different levels of automation, (3) exploration of mitigation strategies versus deliberate use of biases, (4) examination of dynamic and context-dependent manifestations, and (5) validation in high-fidelity simulations or real-world settings.

Most of today’s studies investigating the driver-vehicle interaction of automated driving systems are conducted in simulated environments like driving simulators or virtual reality. While this simulation-based experimental research can produce valuable and valid results, it is at the same time limited by the inherent lack of realism. Important insights into real-world driving experiences and repeated system usage are rarely collected due to the constraints imposed by time and financial resources. In a multi-step research approach, we aim to connect the AutoUI research with real-world users. In the first step, we conducted qualitative interviews with 10 experienced, tech-savvy users of current automated driving systems (Waymo, Cruise, Tesla) and clustered the results into the most important issues from a human factor perspective.

Most of today’s studies investigating the driver-vehicle interaction of automated driving systems are conducted in simulated environments like driving simulators or virtual reality. While this simulation-based experimental research can produce valuable and valid results, it is at the same time limited by the inherent lack of realism. Important insights into real-world driving experiences and repeated system usage are rarely collected due to the constraints imposed by time and financial resources. In a two-step research approach, we aim to connect the AutoUI research with real-world users. In the first step, we conducted qualitative interviews with 10 experienced, tech-savvy users of current automated driving systems (Waymo, Cruise, Tesla) and clustered the results into the most important topics from a human factor perspective. On this basis, the workshop now aims to bring these insights into the AutoUI research community to identify the most relevant and urgent issues that should be addressed in the coming years.

The cooperation between humans and automated vehicles (AVs) has emerged as an important perspective for understanding the authority dynamic of future transportation. Previous research indicates that passengers desire to influence the AV’s behavior in certain traffic situations. However, automation could counter this desire for control by providing more detailed explanations of its behavior and actions. Consequently, this paper investigates the potential relationship between an AV’s level of explainability and passengers’ desire for control to intervene in certain traffic situations while also examining the impact on passengers’ perception concerning UX, trust, and acceptance of AVs. Preliminary results from a virtual reality driving simulator study (N = 50) indicate that the level of explainability of an AV does significantly influence passengers’ desire to intervene or their perception of control over the vehicle. It can also be observed that the level of explainability significantly impacts the user experience, trust in AVs, and perceived safety.

As driving assistance driving systems become increasingly advanced, a correct understanding of the functionality of these systems is crucial for safe use. In this work we explored drivers’ explanation needs and current explanation methods from an important but underlooked perspective: driver training and vehicle acquisition. In a two-step approach, we conducted expert interviews with n = 7 driving instructors and vehicle salespeople in Germany and validated these results with an online survey of n = 105. Our results show that Driver Assistance Systems (DASs) and Advanced Driver Assistance Systems (ADASs), are currently covered in both driver training and vehicle acquisition but to a varying extent and in a very application-oriented manner. A drivers’ tendency for preferring comparative explanations that build upon knowledge about similar systems was found. Based on the combined results, we emphasize the need for mandatory and standardized explanation methods to ensure a safe transition to automated driving.

Human-centered XAI (HCXAI) advocates that algorithmic transparency alone is not sufficient for making AI explainable. Explainability of AI is more than just “opening” the black box — who opens it matters just as much, if not more, as the ways of opening it. In the era of Large Language Models (LLMs), is “opening the black box” still a realistic goal for XAI? In this fourth CHI workshop on Human-centered XAI (HCXAI), we build on the maturation through the previous three installments to craft the coming-of-age story of HCXAI in the era of Large Language Models (LLMs). We aim towards actionable interventions that recognize both affordances and pitfalls of XAI. The goal of the fourth installment is to question how XAI assumptions fare in the era of LLMs and examine how human-centered perspectives can be operationalized at the conceptual, methodological, and technical levels. Encouraging holistic (historical, sociological, and technical) approaches, we emphasize “operationalizing.” We seek actionable analysis frameworks, concrete design guidelines, transferable evaluation methods, and principles for accountability.