TY - JOUR A1 - Feier, Till A1 - Gogoll, Jan A1 - Uhl, Matthias T1 - Hiding Behind Machines: Artificial Agents May Help to Evade Punishment JF - Science and Engineering Ethics N2 - The transfer of tasks with sometimes far-reaching implications to autonomous systems raises a number of ethical questions. In addition to fundamental questions about the moral agency of these systems, behavioral issues arise. We investigate the empirically accessible question of whether the imposition of harm by an agent is systematically judged differently when the agent is artificial and not human. The results of a laboratory experiment suggest that decision-makers can actually avoid punishment more easily by delegating to machines than by delegating to other people. Our results imply that the availability of artificial agents could provide stronger incentives for decision-makers to delegate sensitive decisions. UR - https://doi.org/10.1007/s11948-022-00372-7 KW - automation KW - ethics KW - experiment KW - responsibility KW - algorithm Y1 - 2022 UR - https://doi.org/10.1007/s11948-022-00372-7 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-23155 SN - 1471-5546 SN - 1353-3452 VL - 28 IS - 2 PB - Springer Nature CY - Cham ER - TY - JOUR A1 - Jauernig, Johanna A1 - Uhl, Matthias A1 - Waldhof, Gabi T1 - Genetically Engineered Foods and Moral Absolutism: A Representative Study from Germany JF - Science and Engineering Ethics N2 - There is an ongoing debate about genetic engineering (GE) in food production. Supporters argue that it makes crops more resilient to stresses, such as drought or pests, and should be considered by researchers as a technology to address issues of global food security, whereas opponents put forward that GE crops serve only the economic interests of transnational agrifood-firms and have not yet delivered on their promises to address food shortage and nutrient supply. To address discourse failure regarding the GE debate, research needs to understand better what drives the divergent positions and which moral attitudes fuel the mental models of GE supporters and opponents. Hence, this study investigates moral attitudes regarding GE opposition and support in Germany. Results show that GE opponents are significantly more absolutist than supporters and significantly less likely to hold outcome-based views. Furthermore, GE opponents are more willing to donate for preventing GE admission than supporters are willing to donate for promoting GE admission. Our results shed light on why the divide between opponents and supporters in the German GE debate could remain stark and stable for so long. UR - https://doi.org/10.1007/s11948-023-00454-0 KW - Genetic engineering (GE) KW - Moral absolutism KW - Technology aversion KW - Moral convictions KW - GE debate KW - Consumer skepticism Y1 - 2023 UR - https://doi.org/10.1007/s11948-023-00454-0 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-39681 SN - 1471-5546 SN - 1353-3452 VL - 29 IS - 5 PB - Springer CY - Dordrecht ER - TY - JOUR A1 - Schönmann, Manuela A1 - Bodenschatz, Anja A1 - Uhl, Matthias A1 - Walkowitz, Gari T1 - The Care-Dependent are Less Averse to Care Robots: An Empirical Comparison of Attitudes JF - International Journal of Social Robotics N2 - A growing gap is emerging between the supply of and demand for professional caregivers, not least because of the ever-increasing average age of the world’s population. One strategy to address this growing gap in many regions is the use of care robots. Although there have been numerous ethical debates about the use of robots in nursing and elderly care, an important question remains unexamined: how do the potential recipients of such care perceive situations with care robots compared to situations with human caregivers? Using a large-scale experimental vignette study, we investigated people’s affective attitudes toward care robots. Specifically, we studied the influence of the caregiver’s nature on participants’ perceived comfort levels when confronted with different care scenarios in nursing homes. Our results show that the care-robot-related views of actual care recipients (i.e., people who are already affected by care dependency) differ substantially from the views of people who are not affected by care dependency. Those who do not (yet) rely on care placed care robots’ value far below that of human caregivers, especially in a service-oriented care scenario. This devaluation was not found among care recipients, whose perceived level of comfort was not influenced by the caregiver’s nature. These findings also proved robust when controlled for people’s gender, age, and general attitudes toward robots. UR - https://doi.org/10.1007/s12369-023-01003-2 KW - Care robots KW - Nursing care KW - Robot aversion KW - Affective attitudes KW - Vignette experiment Y1 - 2023 UR - https://doi.org/10.1007/s12369-023-01003-2 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-38099 SN - 1875-4805 VL - 15 IS - 6 SP - 1007 EP - 1024 PB - Springer CY - Heidelberg ER - TY - JOUR A1 - Krügel, Sebastian A1 - Uhl, Matthias T1 - Internal whistleblowing systems without proper sanctions may backfire JF - Journal of Business Economics N2 - Internal whistleblowing systems are supposed to fight misconduct within organizations. Because it is difficult to study their efficacy in the field, scientific evidence on their performance is rare. This is problematic, because these systems bind substantial resources and might generate the erroneous impression of compliance in a company in which misconduct is prevalent. We therefore suggest a versatilely extendable experimental workhorse that allows the systematic study of internal whistleblowing systems in the lab. As a first step, we tested the efficacy of whistleblowing systems if internal punishment for misconduct is mild and hesitant which is usually the case in practice, as several fraud surveys confirm. Our results show that under these conditions almost nobody blew the whistle, and misconduct occurred even more frequently with than without a whistleblowing system. The institutionalization of whistleblowing seemed to crowd out the intrinsic motivation to act compliantly. Moreover, when a whistleblowing system was either unavailable or not used, misconduct was highly contagious and spread quickly. Yet, when we implemented severe and ensured punishment for misconduct, whistleblowing systems could deter wrongdoing. In such a setting, people were willing to blow the whistle and the prevalence of misconduct dropped substantially. Altogether, our results highlight the interaction between institutions and preferences and can support the design of compliance measures within organizations. For compliance managers a key takeaway is that if companies preach a zero-tolerance policy, they should practice it as well. Otherwise, they might even worsen the situation. UR - https://doi.org/10.1007/s11573-023-01144-w KW - Misconduct KW - Whistleblowing KW - Punishment KW - Crowding out Y1 - 2023 UR - https://doi.org/10.1007/s11573-023-01144-w UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-32074 SN - 1861-8928 SN - 0044-2372 VL - 93 IS - 8 SP - 1355 EP - 1383 PB - Springer CY - Wiesbaden ER - TY - JOUR A1 - Krügel, Sebastian A1 - Ostermaier, Andreas A1 - Uhl, Matthias T1 - Algorithms as partners in crime: A lesson in ethics by design JF - Computers in Human Behavior N2 - The human in the loop is often advocated as a panacea against concerns about AI-powered machines, which increasingly take decisions of consequence in all realms of life. However, can we rely on humans to prevent unethical decisions by machines? We run online experiments modeling both the case where the machine serves as a corrective to the human and where the human serves as a corrective to the machine. Our results suggest that, in the former case, humans make similar decisions whether the corrective is a machine or another human. In the latter case, humans take advantage of rather than correct bad decisions by machines, turning into partners in crime. These findings caution us not to count too much on the human in the loop as a moral corrective. Instead, they tend to argue for human–machine decision-making where the human makes the decision and the machine is the corrective. UR - https://doi.org/10.1016/j.chb.2022.107483 KW - Algorithm KW - Artificial intelligence KW - Ethics KW - HITL KW - Human in the loop Y1 - 2022 UR - https://doi.org/10.1016/j.chb.2022.107483 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-28800 SN - 0747-5632 VL - 2023 IS - 138 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Krügel, Sebastian A1 - Uhl, Matthias T1 - Is only one of my selves authentic? An empirical approach JF - Journal of Behavioral and Experimental Economics N2 - In behavioral economics, intrapersonal conflict is predominantly interpreted hierarchically. A “present-biased” intrapersonal doer may spoil the goal of a rational planner. This often suggests paternalistic interventions that help the “true” or “authentic” self to overwhelm its present-biased alter ego. Game theorist Schelling proposed a reciprocal interpretation of intrapersonal conflict that interprets both selves as strategic players, which Elster contradicted by claiming that in any conflict only one self is capable of strategic behavior and therefore authentic. Previous empirical studies, however, cannot test this interpretation, because their design provides commitment devices unilaterally to only one self. In an experiment, we provided commitment devices to both selves and find similar inclinations to use this strategic tool. Given this, the symmetric view on intrapersonal conflict seems no less plausible than the hierarchical one. Our results might contribute to a richer debate on intrapersonal conflict by feeding in some skepticism about the self-evidence with which paternalists take sides. UR - https://doi.org/10.1016/j.socec.2022.101971 KW - Self-commitment KW - Self-binding KW - Intrapersonal conflict KW - Conflict of selves KW - Libertarian paternalism KW - Consumer sovereignty Y1 - 2022 UR - https://doi.org/10.1016/j.socec.2022.101971 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-32063 SN - 2214-8051 SN - 2214-8043 VL - 2023 IS - 102 PB - Elsevier CY - Amsterdam ER - TY - INPR A1 - Krügel, Sebastian A1 - Ostermaier, Andreas A1 - Uhl, Matthias T1 - The moral authority of ChatGPT N2 - ChatGPT is not only fun to chat with, but it also searches information, answers questions, and gives advice. With consistent moral advice, it might improve the moral judgment and decisions of users, who often hold contradictory moral beliefs. Unfortunately, ChatGPT turns out highly inconsistent as a moral advisor. Nonetheless, it influences users' moral judgment, we find in an experiment, even if they know they are advised by a chatting bot, and they underestimate how much they are influenced. Thus, ChatGPT threatens to corrupt rather than improves users' judgment. These findings raise the question of how to ensure the responsible use of ChatGPT and similar AI. Transparency is often touted but seems ineffective. We propose training to improve digital literacy. UR - https://doi.org/10.48550/arXiv.2301.07098 KW - AI KW - ChatGPT KW - ethics KW - moral dilemma Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2301.07098 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-32085 PB - arXiv CY - Ithaca ER - TY - JOUR A1 - Schönmann, Manuela A1 - Bodenschatz, Anja A1 - Uhl, Matthias A1 - Walkowitz, Gari T1 - Contagious humans: A pandemic's positive effect on attitudes towards care robots JF - Technology in Society N2 - History has shown that attitudes toward new technologies can change abruptly following disruptive events. During the COVID-19 pandemic, it became apparent that care robots enable increased social isolation. This feature of robotic care usually raises strong ethical concerns about potentially decreased comfort for the care-dependent. In a large-scale online study, we tested the influence of the pandemic on people's affective attitudes toward care robots. In vignettes on different care scenarios, we measured participants' perceived comfort levels in situations with care robots and human caregivers while controlling for their fear of infection with a viral disease. We found that people generally feel less comfortable with a care robot than with a human caregiver. However, those who had a strong fear of being infected during the pandemic did not devalue a care robot compared to a human caregiver. While care robots remain ethically contested, this study shows that affective attitudes toward care robots may change significantly if they can address an urgent need. UR - https://doi.org/10.1016/j.techsoc.2024.102464 Y1 - 2024 UR - https://doi.org/10.1016/j.techsoc.2024.102464 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-43998 SN - 1879-3274 VL - 2024 IS - 76 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Krügel, Sebastian A1 - Ostermaier, Andreas A1 - Uhl, Matthias T1 - ChatGPT’s inconsistent moral advice influences users’ judgment JF - Scientific Reports N2 - AbstractChatGPT is not only fun to chat with, but it also searches information, answers questions, and gives advice. With consistent moral advice, it can improve the moral judgment and decisions of users. Unfortunately, ChatGPT’s advice is not consistent. Nonetheless, it does influence users’ moral judgment, we find in an experiment, even if they know they are advised by a chatting bot, and they underestimate how much they are influenced. Thus, ChatGPT corrupts rather than improves its users’ moral judgment. While these findings call for better design of ChatGPT and similar bots, we also propose training to improve users’ digital literacy as a remedy. Transparency, however, is not sufficient to enable the responsible use of AI. UR - https://doi.org/10.1038/s41598-023-31341-0 Y1 - 2023 UR - https://doi.org/10.1038/s41598-023-31341-0 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-41835 SN - 2045-2322 VL - 13 PB - Springer Nature CY - London ER - TY - CHAP A1 - Rosbach, Emely A1 - Ammeling, Jonas A1 - Krügel, Sebastian A1 - Kießig, Angelika A1 - Fritz, Alexis A1 - Ganz, Jonathan A1 - Puget, Chloé A1 - Donovan, Taryn A1 - Klang, Andrea A1 - Köller, Maximilian C. A1 - Bolfa, Pompei A1 - Tecilla, Marco A1 - Denk, Daniela A1 - Kiupel, Matti A1 - Paraschou, Georgios A1 - Kok, Mun Keong A1 - Haake, Alexander F. H. A1 - de Krijger, Ronald R. A1 - Sonnen, Andreas F.-P. A1 - Kasantikul, Tanit A1 - Dorrestein, Gerry M. A1 - Smedley, Rebecca C. A1 - Stathonikos, Nikolas A1 - Uhl, Matthias A1 - Bertram, Christof A1 - Riener, Andreas A1 - Aubreville, Marc ED - Yamashita, Naomi ED - Evers, Vanessa ED - Yatani, Koji ED - Ding, Xianghua ED - Lee, Bongshin ED - Chetty, Marshini ED - Toups-Dugas, Phoebe T1 - "When Two Wrongs Don't Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology T2 - CHI'25: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems N2 - Artificial intelligence (AI)-based decision support systems hold promise for enhancing diagnostic accuracy and efficiency in computational pathology. However, human-AI collaboration can introduce and amplify cognitive biases, like confirmation bias caused by false confirmation when erroneous human opinions are reinforced by inaccurate AI output. This bias may increase under time pressure, a ubiquitous factor in routine pathology, as it strains practitioners’ cognitive resources. We quantified confirmation bias triggered by AI-induced false confirmation and examined the role of time constraints in a web-based experiment, where trained pathology experts (n=28) estimated tumor cell percentages. Our results suggest that AI integration fuels confirmation bias, evidenced by a statistically significant positive linear-mixed-effects model coefficient linking AI recommendations mirroring flawed human judgment and alignment with system advice. Conversely, time pressure appeared to weaken this relationship. These findings highlight potential risks of AI in healthcare and aim to support the safe integration of clinical decision support systems. UR - https://doi.org/10.1145/3706598.3713319 Y1 - 2025 UR - https://doi.org/10.1145/3706598.3713319 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58797 SN - 979-8-4007-1394-1 PB - ACM CY - New York ER - TY - INPR A1 - Kammerlander, Calvin A1 - Kolb, Viola A1 - Luegmair, Marinus A1 - Scheermann, Lou A1 - Schmailzl, Maximilian A1 - Seufert, Marco A1 - Zhang, Jiayun A1 - Dalic, Denis A1 - Schön, Torsten T1 - Machine Learning Models for Soil Parameter Prediction Based on Satellite, Weather, Clay and Yield Data N2 - Efficient nutrient management and precise fertilization are essential for advancing modern agriculture, particularly in regions striving to optimize crop yields sustainably. The AgroLens project endeavors to address this challenge by develop ing Machine Learning (ML)-based methodologies to predict soil nutrient levels without reliance on laboratory tests. By leveraging state of the art techniques, the project lays a foundation for acionable insights to improve agricultural productivity in resource-constrained areas, such as Africa. The approach begins with the development of a robust European model using the LUCAS Soil dataset and Sentinel-2 satellite imagery to estimate key soil properties, including phosphorus, potassium, nitrogen, and pH levels. This model is then enhanced by integrating supplementary features, such as weather data, harvest rates, and Clay AI-generated embeddings. This report details the methodological framework, data preprocessing strategies, and ML pipelines employed in this project. Advanced algorithms, including Random Forests, Extreme Gradient Boosting (XGBoost), and Fully Connected Neural Networks (FCNN), were implemented and finetuned for precise nutrient prediction. Results showcase robust model performance, with root mean square error values meeting stringent accuracy thresholds. By establishing a reproducible and scalable pipeline for soil nutrient prediction, this research paves the way for transformative agricultural applications, including precision fertilization and improved resource allocation in underresourced regions like Africa. UR - https://doi.org/10.48550/arXiv.2503.22276 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2503.22276 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59345 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Heinl, Patrizia A1 - Patapovas, Andrius A1 - Pilgermann, Michael T1 - Towards AI-enabled Cyber Threat Assessment in the Health Sector N2 - Cyber attacks on the healthcare industry can have tremendous consequences and the attack surface expands continuously. In order to handle the steadily rising workload, an expanding amount of analog processes in healthcare institutions is digitized. Despite regulations becoming stricter, not all existing infrastructure is sufficiently protected against cyber attacks. With an increasing number of devices and digital processes, the system and network landscape becomes more complex and harder to manage and therefore also more difficult to protect. The aim of this project is to introduce an AI-enabled platform that collects security relevant information from the outside of a health organization, analyzes it, delivers a risk score and supports decision makers in healthcare institutions to optimize investment choices for security measures. Therefore, an architecture of such a platform is designed, relevant information sources are identified, and AI methods for relevant data collection, selection, and risk scoring are explored. UR - https://doi.org/10.48550/arXiv.2409.12765 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2409.12765 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-58381 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Barbosa da Silva, Leonardo A1 - Lobo, Silas A1 - Fernández, Evelio A1 - Facchi, Christian ED - Vinel, Alexey ED - Berns, Karsten ED - Ploeg, Jeroen ED - Gusikhin, Oleg T1 - What Is the Right Bounding Box of a VRU Cluster in V2X Communication? How to Form a Good Shape? T2 - Vehits 2024: 10th International Conference on Vehicle Technology and Intelligent Transport Systems Proceedings N2 - Among the possible traffic members on a Vehicle-to-Everything network, the term Vulnerable Road User (VRU) is assigned e.g. to pedestrians and cyclists. The VRU Awareness Message (VAM) is used by VRUs to inform other users of their presence and ensure they are perceived in a traffic system. Since the number of VRUs in crowded areas might be very high, the over-the-air traffic might be overloaded. To reduce channel overload, VAMs offer a clustering feature in which VRUs with similar kinematics and positions can group themselves so that only one device transmits messages. The VRU Basic Service specification describes the cluster as a bounding box that must cover all its members using a geometric shape so that other vehicles in the vicinity can avoid colliding with the contained VRUs. This paper contributes to the standardization effort by introducing a data structure, the Cluster Map, for the clustering in the VRU Basic Service. Furthermore, this work is the first to suggest strategies for forming bounding box shapes. Simulation results show that each of the geometry types is useful in different situations, thus further research on the topic is advised. UR - https://doi.org/10.5220/0012699100003702 Y1 - 2024 UR - https://doi.org/10.5220/0012699100003702 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-48553 SN - 978-989-758-703-0 SP - 144 EP - 155 PB - SciTePress CY - Setúbal ER - TY - CHAP A1 - Michahelles, Florian A1 - Riener, Andreas A1 - Grundel, Ida A1 - Boztepe, Suzan A1 - Trygg, Kristina A1 - Israel, Habakuk A1 - Pfleging, Bastian A1 - Veenstra, Mettina A1 - Wintersberger, Philipp T1 - Urban-Engage: Pioneering Urban Planning with Citizen-Driven 15-Minute City Solutions T2 - Mensch und Computer 2024 – Workshopband N2 - We propose an innovative approach to empower urban planners by integrating comprehensive and qualified citizen input into the planning of the 15-minute city (15mC) through immersive digital technologies. Our methodology includes (1) enabling citizens to annotate their real environment using augmented reality, (2) generating urban space alternatives based on generative AI and citizen annotations, (3) allowing modifications to AI-generated alternatives, (4) providing immersive 3D simulation environments for experiencing these alternatives, and (5) facilitating better negotiation between citizens and political players to identify the optimal solution. This process aims to account for diverse stakeholder interests, ensuring inclusive contributions and an experiential understanding of potential urban adaptation. UR - https://doi.org/10.18420/muc2024-mci-ws16-393 Y1 - 2024 UR - https://doi.org/10.18420/muc2024-mci-ws16-393 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-52073 PB - Gesellschaft für Informatik CY - Bonn ER - TY - INPR A1 - Paula, Daniel A1 - Bauder, Maximilian A1 - Pfeilschifter, Claus A1 - Petermeier, Franziska A1 - Kubjatko, Tibor A1 - Böhm, Klaus A1 - Riener, Andreas A1 - Schweiger, Hans-Georg T1 - Impact of Partially Automated Driving Functions on Forensic Accident Reconstruction: A Simulator Study on Driver Reaction Behavior in the Event of a Malfunctioning System Behavior N2 - Partially automated driving functions (SAE Level 2) can control a vehicle's longitudinal and lateral movements. However, taking over the driving task involves automation risks that the driver must manage. In severe accidents, the driver's ability to avoid a collision must be assessed, considering their expected reaction behavior. The primary goal of this study is to generate essential data on driver reaction behavior in case of malfunctions in partially automated driving functions for use in legal affairs. A simulator study with two scenarios involving 32 subjects was conducted for this purpose. The first scenario investigated driver reactions to system limitations during cornering. The second scenario examined driver responses to phantom braking caused by the AEBS. As a result, the first scenario shows that none of the subjects could control the situation safely. Due to partial automation, we could also identify a new part of the reaction time, the hands-on time, which leads to increased steering reaction times of 1.18 to 1.74 seconds. In the second scenario, we found that 25 of the 32 subjects could not override the phantom braking by pressing the accelerator pedal, although 16 subjects were informed about the system analog to the actual vehicle manuals. Overall, the study suggests that the current legal perspective on vehicle control and the expected driver reaction behavior for accident avoidance should be reconsidered. UR - https://doi.org/10.20944/preprints202311.0947.v1 Y1 - 2023 UR - https://doi.org/10.20944/preprints202311.0947.v1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-42173 PB - Preprints CY - Basel ER -