Contributions in Merlin

Digital Editions (DE) are scholarly document collections that make research artifacts accessible to both humans and machines in a structured manner, enriched with annotations. However, the interoperability and reusability of DE can be hampered by annotation inconsistencies within DE and heterogeneous annotation practices across DE. We present Tag-Xplore, an interactive and visual exploration tool for annotation practices within and across DE. Tag-Xplore offers multiple coordinated views that provide both attribute-based and document-based access to the huge search space at multiple granularities. The approach also provides rank, filter, and comparison techniques, to further support the exploration. With Tag-Xplore, data curators can validate assumptions based on existing knowledge and generate new insights about annotation practices. We demonstrate the usefulness of Tag-Xplore with two qualitative case studies on attribute ambiguity and outlier documents

From deciding which meal to prepare for our guests to trading-off the pro-environmental effects of climate protection measures against their economic costs, we often must consider the consequences of our actions for the well-being of others (welfare). Vexingly, the tastes and views of others can vary widely. To maximize welfare according to the utilitarian philosophical tradition, decision makers facing conflicting preferences of others should choose the option that maximizes the sum of subjective value (utility) of the entire group. This notion requires comparing intensities of preferences across individuals. However, it remains unclear whether such comparisons are possible at all, and (if they are possible) how they might be implemented in the brain. Here, we show that female and male participants can both learn the preferences of others by observing their choices, and represent these preferences on a common scale to make utilitarian welfare decisions. On the neural level, multivariate support vector regressions revealed that a distributed activity pattern in the ventromedial prefrontal cortex (VMPFC), a brain region previously associated with reward processing, represented preference strength of others. Strikingly, also the utilitarian welfare of others was represented in the VMPFC and relied on the same neural code as the estimated preferences of others. Together, our findings reveal that humans can behave as if they maximized utilitarian welfare using a specific utility representation and that the brain enables such choices by repurposing neural machinery processing the reward others receive.Significance statementIn many situations politicians and civilians strive to maximize the welfare of social groups. If the preferences of group members are in conflict, identifying the utilitarian welfare-maximizing option requires that decision makers can compare the strengths of conflicting preferences on a common scale. Yet, there is a fundamental lack of understanding which brain mechanisms enable such comparisons of conflicting utilities. Here, we show that brain regions involved in reward processing compute welfare comparisons by representing the preferences of others with a common neural code. This provides a neurobiological mechanism to compute utilitarian welfare maximization as desired by moral philosophy in the Humean tradition.

In light of recent growth in privacy awareness and data ownership rights, differential privacy (DP) has emerged as a promising technique employed by several well-known data controller entities. This raises the question of how casual users, as the immediate recipients of privacy threats and risks, comprehend and perceive DP and its key parameter ε, as DP's provided protection depends on it. Existing studies show that ordinary users have the potential to understand the fundamental mechanism of DP and its implications for the privacy-utility trade-off when they are communicated clearly through textual and visual aids and, accordingly, make informed decisions about sharing their data under DP protection. However, these attempts either only implicitly mention a few possible values for ε, such as low, medium, and high, or altogether leave it out of the communication. In this paper, we conduct a between-subject user study (N=426) to investigate the effectiveness of nine interactive visual tools to communicate ε explicitly and on a continuous scale in a data-sharing scenario related to publishing positive COVID-19 test results. These interactive visual tools allow casual users to visualize DP's effects on data accuracy and/or privacy loss for various ε values. We found that visualizations incorporating the privacy loss component have a significant impact on assisting users in selecting values that are closer to the recommended values by experts. However, depending on the ratio between DP noise and underlying data, the accuracy loss component disparately affects users' ε decision; the bigger the relative error, the bigger the selected epsilon and vice versa. Thus, accuracy portrayals should be carried out with care. We contextualize our findings in the existing literature and conclude with insights and recommendations on effectively employing our findings to communicate DP to casual users.

This paper explores the influence of cultural factors on mental health help-seeking behaviors and the subsequent implications for the design of mental health technologies. Using Hofstede’s Cultural Dimensions as a framework, we conducted a comprehensive literature review to examine how cultural variations affect patient behaviors in seeking mental health support. This review categorically analyses literature corresponding to each of Hofstede’s five dimensions – Power Distance, Individualism vs. Collectivism, Masculinity vs. Femininity, Uncertainty Avoidance, and Long-Term Orientation. The findings reveal significant cultural influences on help-seeking behaviors, highlighting the need for culturally sensitive approaches in mental health technology design. This study underscores the importance of cultural awareness in the design and deployment of mental health technologies, offering insights for future research and development in this field.

In today’s digital age, characterized by rapid news consumption and increasing vulnerability to propaganda, fostering citizens' critical thinking is crucial for stable democracies. This paper introduces the design of ClarifAI, a novel automated propaganda detection tool designed to nudge readers towards more critical news consumption by activating the analytical mode of thinking, following Kahneman's dual-system theory of cognition. Using Large Language Models, ClarifAI detects propaganda in news articles and provides context-rich explanations, enhancing users' understanding and critical thinking. Our contribution is threefold: first, we propose the design of ClarifAI; second, in an online experiment, we demonstrate that this design effectively encourages news readers to engage in more critical reading; and third, we emphasize the value of explanations for fostering critical thinking. The study thus offers both a practical tool and useful design knowledge for mitigating propaganda in digital news.

To call attention to and motivate action on ethical issues in business or society, messengers often criticize groups for wrongdoing and ask these groups to change their behavior. When criticizing target groups, messengers frequently identify and express concern about harm caused to a victim group, and in the process address a target group by criticizing them for causing this harm and imploring them to change. However, we find that when messengers criticize a target group for causing harm to a victim group in this way—expressing singular concern for the victim group—members of the target group infer, often incorrectly, that the messenger views the target group as less moral and unworthy of concern. This inferred lack of moral concern reduces criticism acceptance and prompts backlash from the target group. To address this problem, we introduce dual concern messaging—messages that simultaneously communicate that a target group causes harm to a victim group and express concern for the target group. A series of several experiments demonstrate that dual concern messages reduce inferences that a critical messenger lacks moral concern for the criticized target group, increase the persuasiveness of the criticism among members of the target group, and reduce backlash from consumers against a corporate messenger. When pursuing justice for victims of a target group, dual concern messages that communicate concern for the victim group as well as the target group are more effective in fostering openness toward criticism, rather than defensiveness, in a target group, thus setting the stage for change.

We contribute to the literature on statistical robustness of risk measures by computing the index of qualitative robustness for risk measures based on utility functions. This problem is intimately related to finding the natural domain of finiteness and continuity of such risk measures.

Search engines serve as information gatekeepers on a multitude of topics dealing with different aspects of society. However, the ways search engines filter and rank information are prone to biases related to gender, ethnicity, and race. In this article, we conduct a systematic algorithm audit to examine how one specific form of bias, namely, sexualization, is manifested in Google’s text search results about different national and gender groups. We find evidence of the sexualization of women, particularly those from the Global South and East, in search outputs in both organic and sponsored search results. Our findings contribute to research on the sexualization of people in different forms of media, bias in web search, and algorithm auditing as well as have important implications for the ongoing debates about the responsibility of transnational tech companies for preventing systems they design from amplifying discrimination.

The COVID-19 pandemic demonstrated the fragility of international, national, regional, and local risk management systems. It revealed an urgent need to improve risk planning, preparedness, and communication strategies. In parallel, it created an opportunity to drastically re-think and transform societal processes and policies to prevent future shocks originating not only from health, but also combined with those related to climate change and biodiversity loss. In this perspective, we examine how to improve integrated risk assessment and management (IRAM) capacities to address interconnected shocks. We present the results from a series of workshops within the framework of the University of Zurich and University of Geneva. Initiative "Shaping Resilient Societies: A Multi-Stakeholder Approach to Create a Responsive Society". This initiative gathered experts from multiple disciplines to discuss their perspectives on resilience; here we present the key messages of the "Pandemics, Climate and Sustainability” thinking group. We identify a roadmap and selected research areas concerning the improvement of IRAM analysis capacities, practices, policies. We recommend the development of robust data systems and science-policy advice systems to address combined shocks emerging from health, biodiversity loss and climate change. We posit that further developing the IRAM framework to include these recommendations will improve societal preparedness and response capacity and will provide more empirical evidence supporting decision-making and the selection of strategies and measures for integrated risk reduction.

When a sample combines data from two or more groups, multivariate regression yields a matrix-weighted average of the group-specific coefficient vectors. However, it is possible that the weighted average of a specific coefficient falls outside the range of the group-specific coefficients, and it may even have a different sign compared to both group-level coefficients, a manifestation of Simpson’s paradox. The result of the combined regression is then prone to misinterpretation. The purpose of this paper is to raise awareness of this problem and to state conditions under which such non-convex weighting or sign reversal can arise, for a model with two regressors and two groups. Two illustrative examples, an investment equation estimated with panel data, and a cross-sectional earnings equation for men and women, highlight the relevance of these findings for applied work.