Search Results (133)

Recent research by Yucel and colleagues suggests that children perceive equality-based fairness violations (resources being distributed unequally) as less serious than prototypical moral harms, but that making the harmful consequences of unfairness salient shifts these judgments toward the moral domain. We examined whether merit-based fairness violations (someone receiving less than they earned) would similarly shift judgments toward the moral domain by making the injustice more salient. Replicating prior work, 4-year-old children (N = 62) rated prototypical moral violations as significantly more severe than equality-based fairness violations, which were rated as similar in severity to conventional violations. Contrary to predictions, merit-based fairness violations also showed this pattern: They were judged as less severe than prototypical moral violations and similarly severe as both equality-based fairness violations and conventional violations. Children also did not consistently group either type of fairness violation with moral or conventional violations. These findings contribute to a growing body of evidence that children’s (and adults’) perceptions of fairness—whether equality-based or merit-based—are more nuanced than previously thought and that unfairness may not spontaneously be treated like other, more prototypical moral norm violations. Full article

The integration of AI into journalism has intensified debates about the future of news production, yet existing scholarship has focused predominantly on AI’s capabilities rather than on irreplaceable human competencies. This study shifts analytical focus from replacement to complementarity, investigating the boundaries of AI through the perspectives of both journalists and AI developers. Ten participants—including field reporters, news anchors, broadcast journalists, and AI developers—were interviewed through in-depth, semi-structured interviews. Thematic analysis revealed three core dimensions of irreplaceable human competency: embodied presence and rapport-building, contextual judgment and meaning-making, and investigative initiative requiring moral agency. Practitioners and developers converged on AI’s persistent limitations in factual reliability, emotional authenticity, and ethical accountability. Based on these findings, a three-tier human–AI collaborative model is proposed, allocating computational tasks to AI while preserving human authority over editorial judgment, source relationships, and ethical decisions. These findings contribute to human–machine communication theory, extend algorithmic journalism literature beyond capability assessments, and offer practical implications for newsroom workflow design, journalism education, and AI governance. Findings are situated within the Korean media context and should be interpreted accordingly, with implications that may extend to other broadcasting-oriented journalism cultures. Full article

“Properness, correctness and uprightness” (zheng 正) refers to a common and significant concept in Chinese philosophy. In Chinese philosophical discourse, zheng embodies moral ideals. To date, scholarly attention has focused on compound concepts incorporating zheng, such as “central and zheng” (zhongzheng 中正), “the position of zheng” (zhengwei 正位), and “make the family in accordance with zheng” (zhengjia 正家), as their research objects. However, the independent philosophical meaning of zheng in the Yijing 易經 remains underexplored. Through etymological research and textual analysis, this study reveals three philosophical dimensions of the Yijing. First, it distinguishes zheng from “in correspondence to” (dang 當). It shows that dang refers to a judgment about physical alignment with time and position in theoretical situations, lacking strong moral force. Second, it argues that zheng in the Yijing originates from a metaphysical concept of a perfect ideal, broadly referring to the ideal perfect way (zheng dao 正道). The Yijing emphasizes the metaphysical level of zheng (in accordance with the perfect way), and possesses zheng as a strong moral binding force for continuing self-improvement. However, zheng does not directly function as the presupposed rationale for moral judgments and choices. Third, it examines the way of cultivating zheng (yang zheng zhi dao 養正之道) as a theory of moral cultivation (gongfu 工夫). This practical path, articulated through the hexagrams Meng 蒙 and Yi 頤, is interpreted as a form of purifying the heart/mind (xin 心) to align with the cosmic heart/mind. The study demonstrates that the moral source and moral cultivation process in the Yijing refers to a theory of “cultivating one’s heart/mind (xin 心) through practice”. It provides a perspective for understanding the moral perfectness, heart/mind and morality in the Yijing. Full article

Background/Objectives: The processing and evaluation of behavior, actions or events that go against social (moral) norms can be assumed to operate on mental representations of the world and of how people typically behave. These mechanisms and representations may therefore be shared by the processing of meaning in general. The current study investigated whether the processing of deviations of morality can be distinguished from processing of semantic inconsistencies. Methods: Event-related brain potentials (ERPs) were recorded from English speakers while they read short written texts in English for comprehension. Texts contained words that constituted moral violations, semantic violations and neutral controls depending on the context, allowing for a direct comparison. Results: Using trial-based analyses, we found different ERP responses to semantic and moral violations: the moral violation elicited a long-lasting, posterior Late Positive Component (LPC) starting at around 300 ms, whereas the semantic violation elicited a positivity that started later and was descriptively more frontally distributed. Furthermore, the LPC amplitudes could be explained by the moral acceptance scores over and above plausibility scores, but not vice versa. Conclusions: The outcomes are compatible with the view that the processing of moral deviations engages at least some mechanisms that are different from the processing of semantic deviations. Full article

Our paper presents an intuitive model of bystander intervention to workplace mistreatment. Drawing on the literature on moral intuition, our paper proposes (1) that bystanders match an observed conduct to mistreatment descriptions (the first type of mistreatment prototypes), and (2) that bystanders make intuitive judgments and take immediate interventions when intervention prescriptions (the second type of mistreatment prototypes) exist in their long-term memory. Our paper also argues that bystanders’ intuitive judgments and interventions depend on the accessibility of their mistreatment prototypes, which are formed through learning mechanisms. Our paper contributes to the literature on bystander responses to workplace mistreatment. Full article

Solar Radiation Modification (SRM) is increasingly discussed as a potential supplement to climate-change mitigation, yet public and stakeholder judgments remain sensitive to knowledge, framing, and perceived risks. We examined how a structured university classroom module on SRM reshaped student perceptions using a matched pre–post survey design. Participants were students enrolled in an English-taught global climate change course (N = 106); 103 students provided valid matched responses after applying pre-specified exclusion rules. Self-rated SRM knowledge increased substantially after the module (mean change +0.47 on a 1–3 scale; Wilcoxon signed-rank p (Holm-adjusted) < 1 × 10⁻⁷; Cohen’s dz = 0.67). Support for SRM research remained moderately positive but did not increase (pre mean 3.76 to post mean 3.54 on a 1–5 scale). In contrast, support for stratospheric aerosol injection (SAI) deployment declined (pre mean 3.42 to post mean 2.95; p (Holm-adjusted) = 0.0084; dz = −0.33), and preferences shifted away from prioritizing climate intervention toward low-carbon development (mean change −0.68 on a 1–5 priority scale; p (Holm-adjusted) = 0.0001; dz = −0.45). Post-lecture models indicated that perceived benefits versus risks was the most consistent correlate of support across outcomes. Open-ended responses most frequently emphasized feasibility, unintended consequences, governance, and moral hazard. Overall, students largely endorsed SRM research as valuable while becoming more cautious about deployment and political prioritization, suggesting that balanced, structured instruction can sharpen sensitivity to evidence, uncertainty, and potential trade-offs that students also weighed in the survey. Full article

Color metaphors may shape how people mentally represent abstract legal values such as justice and thereby influence legal socialization and law-related cognition. We tested whether black/white color terms are metaphorically linked to justice conceived specifically as a legal value, and whether these linkages vary with task demands. In two preregistered experiments that controlled for affective valence, word frequency, and semantic relatedness, Experiment 1 employed a Stroop-style lexical-judgment task with law-relevant terms and found faster responses to justice-related (legal) words than to injustice-related words and higher accuracy for white-colored stimuli, but no reliable color × meaning interaction—suggesting the absence of an automatic color–justice congruency effect during early, automatic processing. Experiment 2 used a translation-matching paradigm in which participants selected black or white translations for unfamiliar foreign words; here, participants systematically matched justice-related (legal) items with white and injustice-related items with black at rates above chance, revealing explicit color–justice associations. Together, the results point to a robust mental linkage of white with justice as a legal value, while black–injustice mappings emerge primarily under explicit selection demands. These findings suggest that black/white color metaphors organize law-related moral cognition but are flexibly activated depending on cognitive task and processing level. Full article

This study investigates how South Korean meditation teachers conceptualize core professional competencies in digitally delivered and AI-mediated contemplative contexts, addressing a gap in prior research that has emphasized effectiveness and technological scalability over teachers’ own understandings of authority and professionalism. Using Q methodology, the study identified shared subjective meaning structures among 21 certified meditation teachers in South Korea. From 133 competency-related statements derived from academic literature and practitioner sources, a 33-item Q sample was developed and analyzed through by-person factor analysis. The analysis revealed four distinct perception types of meditation teaching competencies: 1. Embodied Practice-Grounded, prioritizing the depth of personal meditative practice; 2. Relational Presence-Grounded, emphasizing intersubjective attunement between teacher and practitioner; 3. Pedagogical Judgment-Grounded, focusing on the strategic integration of theory and coaching practice; and 4. Ethical Self-Reflection-Grounded, centering on ongoing moral reflexivity and inner examination. The findings indicate that, in the face of AI-driven automation, meditation teaching competence is perceived not as a set of technical skills or digital literacy, but as a “way of being” rooted in the triadic integration of ethical self-awareness, relational presence, and embodied practice. Furthermore, the study suggests that in AI-mediated contemplative environments, professional competence in AI-mediated contemplative environments is defined less by technological adoption than by ethical discernment and responsibility for non-delegable aspects of guidance, advancing a practitioner-centered account of spiritual authority in the era of artificial intelligence. Full article

This study advances understanding of public trust in artificial intelligence (AI) by distinguishing between overall trust in AI as a system and trust in specific AI components, and by disentangling the roles of perceived capacity, risk, and personhood. Drawing on nationally representative survey data from 1099 U.S. adults collected in 2023 (AIMS dataset), the study estimates multiple regression models to examine how these evaluations shape trust across technical, organizational, and institutional dimensions. The results show that perceived cognitive capacity is the strongest positive predictor of both overall and component-level trust, while emotional and autonomous capacity primarily enhances trust in specific system components. Perceived social risk consistently undermines trust across all levels, whereas perceived personal risk mainly erodes trust in technical components. Importantly, support for granting AI legal or institutional status significantly increases trust, while moral consideration of AI exhibits limited direct effects, highlighting a critical distinction between institutional accountability and ethical concern. Together, these findings demonstrate that public trust in AI is not a unitary attitude but reflects multidimensional judgments about capability, risk, and governance. The study underscores the importance of institutional accountability and risk mitigation—alongside transparent communication about AI capabilities—for fostering sustainable public trust in AI. Full article

We examine the moral dilemma of how autonomous vehicles (AVs) should be programmed to act in unavoidable crash scenarios involving trade-offs between saving one life and saving many. We report results from three experimental studies that investigate individuals’ preferences over alternative AV decision rules in stylized crash scenarios. Across designs, we find robust support for a probabilistic decision rule that assigns passengers and pedestrians equal ex ante chances of survival (a 50:50 rule). This preference persists across different framings and remains salient even when additional probabilistic options are introduced. Full article

The increasing integration of artificial intelligence (AI) in decision-making processes has amplified discussions surrounding algorithmic authority—the perceived epistemic legitimacy of AI systems over human judgment. This study investigates how individuals attribute epistemic authority to AI, focusing on psychological, contextual, and sociotechnical factors. Existing research highlights the importance of trust in automation, perceived performance, and moral frameworks in shaping such attributions. Unlike prior conceptual or philosophical accounts of algorithmic authority, our study adopts a relational and empirically grounded perspective by operationalizing algority through psychometric measures and contextual assessments. To address knowledge gaps in the micro-level dynamics of this phenomenon, we conducted an empirical study using psychometric tools and scenario-based assessments. Here, we report key findings from a survey of 610 participants, revealing significant correlations between trust in automation (TiA), perceptions of automated performance (PAS), and the propensity to defer to AI, particularly in high-stakes scenarios like criminal justice and job-matching. Trust in automation emerged as a primary factor, while moral attitudes moderated deference in ethically sensitive contexts. Our findings highlight the practical relevance of transparency and explainability for supporting critical engagement with AI outputs and for informing the design of contextually appropriate decision support. This study contributes to understanding algorithmic authority as a multidimensional construct, offering empirically grounded insights for designing AI systems that are trustworthy and context-sensitive. Full article

This article examines the concepts of war and peace through the lens of Christian philosophy, integrating anthropological, ethical, and socio-political perspectives. It argues that while conflict is rooted in human biology and social organization, war is not a biological necessity but a culturally constructed phenomenon shaped by ideology, power structures, and moral judgment. Drawing on insights from ethology, political theory, and Christian moral theology, the study analyzes the causes of war in modern mass societies, including nationalism, global capitalism, media influence, and environmental degradation. Central attention is given to the Christian tradition’s balanced approach to war and peace, particularly its articulation of just war theory as a middle path between pacifism and militarism. The article contends that war is inherently irrational, though occasionally morally permissible as a penultimate resort aimed at restoring just peace. Ultimately, it presents Christian ethical messianism as a coherent normative framework for evaluating armed conflict, assigning responsibility, and fostering a durable peace grounded in human dignity, justice, and the moral order. Full article

Since 2017, more than a dozen civics institutes have been founded at America’s public universities, marking a renaissance in civic education. Grounded in the liberal arts, these institutes rightly restore the pursuit of knowledge for its own sake and reconnect citizens to the nation’s past. Yet liberal education requires assistance to help students navigate today’s data-driven republic, where questions of law and justice increasingly turn on the interpretation of evidence. This article proposes a balanced model for civics education—a “renewed civic pragmatism”—that unites the historical connectedness of liberal learning with the technical skills required for public life and the rule of law. In doing so, civics education recovers its role as a bridge between moral principle, empirical judgment, and the pursuit of justice under law. Full article

Using Machine Learning (ML) in educational management transforms higher education strategy. This study examines students’ views on machine learning (ML) technologies and how they might be used to plan, monitor, and predict student performance. The Faculty of Industrial Engineering and Robotics surveyed 118 third-year undergraduates. It featured closed- and open-ended questions to collect quantitative and qualitative data. Descriptive statistics showed broad patterns, inferential tests (Chi-square, t-test, ANOVA) showed group differences, regression models predicted school outcomes, and exploratory factor analysis (EFA) and clustering found hidden attitudes and student profiles. A multi-method quantitative approach combining descriptive statistics, inferential tests, regression modeling, and exploratory techniques (EFA and clustering) was employed. The findings show that most students realize that ML may help them be more productive, adapt their study pathways, and learn about the future. Concerns remain regarding its accuracy, overreliance, and morality. The findings indicate that ML can both support and challenge educational management, depending on how responsibly it is implemented. Results show that institutions may utilize ML as a strategic tool to boost academic progress and make better judgments, provided they incorporate it responsibly and follow ethical rules and training. Full article

The dual-process theory of moral judgment asserts that moral judgments come about because of the operation of either of two independent decision processes, often described as a cognitive/rational process and an intuitive/affective process. In some cases, these processes are seen to operate in competition. We trace the development of this account and highlight how the neural and behavioral evidence almost universally relies on the validity of a series of untested statements that, collectively, we call the dual-process assumptions. We show how these assumptions produce experimental methods that cannot falsify the dual-process account. We provide an in-depth and critical analysis of the kind of neurophysiological and behavioral evidence that has been used to support the theory and conclude that it is tenuous, equivocal, or both. Full article