TY - CHAP A1 - Ehsan, Upol A1 - Wintersberger, Philipp A1 - Watkins, Elizabeth Anne A1 - Manger, Carina A1 - Ramos, Gonzalo A. A1 - Weisz, Justin A1 - Daumé III, Hal A1 - Riener, Andreas A1 - Riedl, Mark O. T1 - Human-Centered Explainable AI (HCXAI): Coming of age T2 - CHI EA '23: Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems UR - https://doi.org/10.1145/3544549.3573832 Y1 - 2023 UR - https://doi.org/10.1145/3544549.3573832 SN - 978-1-4503-9422-2 PB - ACM CY - New York ER - TY - CHAP A1 - Dong, Jiayuan A1 - Nadri, Chihab A1 - Alvarez, Ignacio A1 - Diels, Cyriel A1 - Lee, Myeongkyu A1 - Li, Jingyi A1 - Liao, Pei Hsuan A1 - Manger, Carina A1 - Sadeghian, Shadan A1 - Schuß, Martina A1 - Walker, Bruce N. A1 - Walker, Francesco A1 - Wang, Yiyuan A1 - Jeon, Myounghoon T1 - “Play Your Anger”: A Report on the Empathic In-vehicle Interface Workshop T2 - 15th International ACM Conference on Automotive User Interfaces: Adjunct Conference Proceedings UR - https://doi.org/10.1145/3581961.3609865 KW - HCI KW - Empathy KW - Human Vehicle Interaction KW - In-Vehicle User Interface Y1 - 2023 UR - https://doi.org/10.1145/3581961.3609865 SN - 979-8-4007-0112-2 SP - 168 EP - 173 PB - ACM CY - New York ER - TY - CHAP A1 - Ammeling, Jonas A1 - Manger, Carina A1 - Kwaka, Elias A1 - Krügel, Sebastian A1 - Uhl, Matthias A1 - Kießig, Angelika A1 - Fritz, Alexis A1 - Ganz, Jonathan A1 - Riener, Andreas A1 - Bertram, Christof A1 - Breininger, Katharina A1 - Aubreville, Marc ED - Stolze, Markus ED - Loch, Frieder ED - Baldauf, Matthias ED - Alt, Florian ED - Schneegass, Christina ED - Kosch, Thomas ED - Hirzle, Teresa ED - Sadeghian, Shadan ED - Draxler, Fiona ED - Bektas, Kenan ED - Lohan, Katrin ED - Knierim, Pascal T1 - Appealing but Potentially Biasing - Investigation of the Visual Representation of Segmentation Predictions by AI Recommender Systems for Medical Decision Making T2 - Mensch und Computer 2023: Building Bridges: Tagungsband (Proceedings) UR - https://doi.org/10.1145/3603555.3608561 Y1 - 2023 UR - https://doi.org/10.1145/3603555.3608561 SN - 979-8-4007-0771-1 SP - 330 EP - 335 PB - ACM CY - New York ER - TY - CHAP A1 - Manger, Carina A1 - Preiwisch, Anna A1 - Riener, Andreas T1 - Invisible Barriers: Understanding and Supporting the Mobility Needs and Challenges of Individuals with Mental Health Conditions T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3758489 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3758489 SN - 979-8-4007-2014-7 SP - 100 EP - 105 PB - ACM CY - New York ER - TY - CHAP A1 - Peintner, Jakob A1 - Manger, Carina A1 - Alvarez, Ignacio A1 - Riener, Andreas T1 - Enhancing Pedestrian Realism in Adverse-Weather Driving Simulations Using Motion Capture Data T2 - Adjunct Conference Proceedings: 17th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 22-25, 2025, Brisbane, Australia UR - https://doi.org/10.1145/3744335.3758507 Y1 - 2025 UR - https://doi.org/10.1145/3744335.3758507 SN - 979-8-4007-2014-7 SP - 214 EP - 219 PB - ACM CY - New York ER - TY - CHAP A1 - Manger, Carina A1 - Albrecht, Kathrin A1 - Riener, Andreas ED - Maedche, Alexander ED - Beigl, Michael ED - Gerling, Kathrin ED - Mayer, Sven T1 - Explanation Needs in Automated Driving: Insights from German Driving Education and Vehicle Acquisition T2 - MuC'24: Proceedings of the 2024 Conference on Mensch und Computer N2 - 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. UR - https://doi.org/10.1145/3670653.3670679 Y1 - 2024 UR - https://doi.org/10.1145/3670653.3670679 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-50497 SN - 979-8-4007-0998-2 SP - 288 EP - 298 PB - ACM CY - New York ER - TY - JOUR A1 - Peintner, Jakob A1 - Manger, Carina A1 - Berger, Melanie A1 - Riener, Andreas T1 - Balancing transparency and control: The impact of AI explanation detail on user perception in automated vehicles JF - Transportation Research Interdisciplinary Perspectives N2 - As automated vehicle technology advances, explainable AI has emerged as a critical tool to enable users to understand and predict the behavior of AI systems, particularly in safety-critical applications such as automated driving. However, increased transparency in AI explanations may inadvertently contribute to an “illusion of control”, a cognitive bias in which drivers overestimate their influence or understanding of the AI’s actions. We aim to better understand how the level of detail in AI explanations affects users of automated vehicles. In a virtual reality study, N = 44 participants experienced different explanation levels (low, medium, high) in an automated ride (SAE L4) compared to a baseline condition with no explanations. The results show a significant improvement in participants’ user experience, acceptance, and explanation satisfaction, with more detailed explanations. Our findings also indicate that as AI explanations become more detailed, users’ perceived level of control increases significantly, although this perception does not correlate with actual control capabilities. At the same time, it decreased their desire to take control, indicating users’ susceptibility to the ’illusion of control’ bias in the context of automated driving. Overall, this suggests that the design of explanation interfaces should strive for a balanced level of detail that promotes AI transparency without causing cognitive overload. At the same time, explainable AI can be utilized to decrease users’ desire to intervene in the AI’s actions. UR - https://doi.org/10.1016/j.trip.2025.101742 Y1 - 2025 UR - https://doi.org/10.1016/j.trip.2025.101742 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64740 SN - 2590-1982 VL - 2025 IS - 34 PB - Elsevier CY - Amsterdam ER - TY - INPR A1 - Peintner, Jakob A1 - Manger, Carina A1 - Berger, Melanie A1 - Riener, Andreas T1 - Balancing Transparency and Control: The Impact of Ai Explanation Detail on User Perception in Automated Vehicles T2 - SSRN UR - https://dx.doi.org/10.2139/ssrn.5027721 Y1 - 2024 UR - https://dx.doi.org/10.2139/ssrn.5027721 PB - Elsevier CY - Amsterdam ER -