@article{JauernigUhlPies2024, author = {Jauernig, Johanna and Uhl, Matthias and Pies, Ingo}, title = {When Goliath sells to David: explaining price gouging perceptions through power}, volume = {203}, journal = {Public Choice}, number = {1-2}, publisher = {Springer}, address = {New York}, issn = {1573-7101}, doi = {https://doi.org/10.1007/s11127-024-01191-z}, pages = {139 -- 155}, year = {2024}, abstract = {AbstractExternal shocks (e.g., due to a pandemic) may lead to price jumps in the short term. Rather than being read as a signal of increased scarcity, the resulting "price gouging" is often ascribed to sellers' selfish exploitation of the crisis. In our experimental study, we investigate the drivers of fairness perceptions regarding voluntary transactions in situations of increased scarcity and explore how they pertain to the economic policy debate on price gouging restrictions. Departing from previous research, our results show that perceptions of power, not of the seller as the profiteer (mercantilism), drive fairness perceptions. The more powerful a transaction partner is assumed to be, the less the respective transaction is regarded as fair. In line with the literature, we also find that fairness perceptions are correlated with zero-sum thinking (i.e., a denial of the mutuality of benefits implied by voluntary transactions). Our study helps to better understand why some market regulations appear attractive despite suboptimal outcomes, thus revealing a mixing of the micro and the macro cosmos, against which Hayek warned. By casting a light on the psychological mechanisms behind attitudes toward markets, we aim to improve the assessment of legitimacy issues and contribute to explaining (and overcoming) the moral paradox of modernity.}, language = {en} } @article{SchoenmannBodenschatzUhletal.2024, author = {Sch{\"o}nmann, Manuela and Bodenschatz, Anja and Uhl, Matthias and Walkowitz, Gari}, title = {Contagious humans: A pandemic's positive effect on attitudes towards care robots}, volume = {2024}, pages = {102464}, journal = {Technology in Society}, number = {76}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1879-3274}, doi = {https://doi.org/10.1016/j.techsoc.2024.102464}, year = {2024}, abstract = {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.}, language = {en} } @article{KruegelUhl2024, author = {Kr{\"u}gel, Sebastian and Uhl, Matthias}, title = {The risk ethics of autonomous vehicles: an empirical approach}, volume = {14}, pages = {960}, journal = {Scientific Reports}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {https://doi.org/10.1038/s41598-024-51313-2}, year = {2024}, abstract = {How would people distribute risks of autonomous vehicles (AVs) in everyday road traffic? The rich literature on the ethics of autonomous vehicles (AVs) revolves around moral judgments in unavoidable collision scenarios. We argue for extending the debate to driving behaviors in everyday road traffic where ubiquitous ethical questions arise due to the permanent redistribution of risk among road users. This distribution of risks raises ethically relevant questions that cannot be evaded by simple heuristics such as "hitting the brakes." Using an interactive, graphical representation of different traffic situations, we measured participants' preferences on driving maneuvers of AVs in a representative survey in Germany. Our participants' preferences deviated significantly from mere collision avoidance. Interestingly, our participants were willing to take risks themselves for the benefit of other road users, suggesting that the social dilemma of AVs may be mitigated in risky environments. Our research might build a bridge between engineers and philosophers to discuss the ethics of AVs more constructively.}, language = {en} } @unpublished{RosbachAmmelingKruegeletal.2024, author = {Rosbach, Emely and Ammeling, Jonas and Kr{\"u}gel, Sebastian and Kießig, Angelika and Fritz, Alexis and Ganz, Jonathan and Puget, Chlo{\´e} and Donovan, Taryn and Klang, Andrea and K{\"o}ller, Maximilian C. and Bolfa, Pompei and Tecilla, Marco and Denk, Daniela and Kiupel, Matti and Paraschou, Georgios and Kok, Mun Keong and Haake, Alexander F. H. and de Krijger, Ronald R. and Sonnen, Andreas F.-P. and Kasantikul, Tanit and Dorrestein, Gerry M. and Smedley, Rebecca C. and Stathonikos, Nikolas and Uhl, Matthias and Bertram, Christof and Riener, Andreas and Aubreville, Marc}, title = {"When TwoWrongs Don't Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2411.01007}, year = {2024}, language = {en} } @article{AmmelingAubrevilleFritzetal.2024, author = {Ammeling, Jonas and Aubreville, Marc and Fritz, Alexis and Kießig, Angelika and Kr{\"u}gel, Sebastian and Uhl, Matthias}, title = {An interdisciplinary perspective on AI-supported decision making in medicine}, volume = {2025}, pages = {102791}, journal = {Technology in Society}, number = {81}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1879-3274}, doi = {https://doi.org/10.1016/j.techsoc.2024.102791}, year = {2024}, abstract = {Artificial intelligence (AI)-supported medical diagnosis offers the potential to utilize the collaborative intelligence of context-sensitive humans and narrowly focused machines for patients' benefit. The employment of machine-learning-based decision-support systems (MLDSS) in medicine, however, raises important multidisciplinary challenges that cannot be addressed in isolation. We discuss three disciplinary perspectives on the topic and their interplay. Ethical issues arise at the level of changing responsibility structures in healthcare. Behavioral issues relate to the actual impact that the system has on physicians. Technical issues arise with respect to the training of a machine learning (ML) model that gives accurate advice. We argue that the interaction between physicians and MLDSS including the concrete design of the interface in which this interaction occurs can only be considered at the intersection of all three disciplines.}, language = {en} } @article{KruegelAmmelingAubrevilleetal.2024, author = {Kr{\"u}gel, Sebastian and Ammeling, Jonas and Aubreville, Marc and Fritz, Alexis and Kießig, Angelika and Uhl, Matthias}, title = {Perceived responsibility in AI-supported medicine}, volume = {40}, journal = {AI \& Society: Journal of Knowledge, Culture and Communication}, publisher = {Springer}, address = {London}, issn = {1435-5655}, doi = {https://doi.org/10.1007/s00146-024-01972-6}, pages = {1485 -- 1495}, year = {2024}, abstract = {In a representative vignette study in Germany with 1,653 respondents, we investigated laypeople's attribution of moral responsibility in collaborative medical diagnosis. Specifically, we compare people's judgments in a setting in which physicians are supported by an AI-based recommender system to a setting in which they are supported by a human colleague. It turns out that people tend to attribute moral responsibility to the artificial agent, although this is traditionally considered a category mistake in normative ethics. This tendency is stronger when people believe that AI may become conscious at some point. In consequence, less responsibility is attributed to human agents in settings with hybrid diagnostic teams than in settings with human-only diagnostic teams. Our findings may have implications for behavior exhibited in contexts of collaborative medical decision making with AI-based as opposed to human recommenders because less responsibility is attributed to agents who have the mental capacity to care about outcomes.}, language = {en} }