Yi Zeng

The Urdu and Pashto term ghairat is often translated into English as ‘honour’, but this translation fails to capture its full ethical depth and cultural specificity. In South Asian and Muslim-majority societies, ghairat refers not simply to an individual’s reputation but to a deeply embedded, gendered moral code of vigilance, modesty and communal dignity. Even Pakistan’s Supreme Court has remarked that ghairat lacks a precise English equivalent, noting that in some contexts, it carries a connotation closer to ‘arrogance’ than ‘honour’.1 Western discourse is most familiar with ghairat through media coverage of ‘honor killings’, where women are killed by family members for perceived moral lapses. While such acts are undeniably brutal and ethically indefensible, they reflect a distorted and extreme manifestation of ghairat. The concept itself operates much more widely and subtly in everyday decisions, shaping family roles, gender norms and even access to healthcare. In its idealised form, ghairat embodies dignity and a sense of moral responsibility to protect family and community integrity. In its harmful form, it can become a mechanism of control and oppression, especially for women, limiting autonomy and delaying or denying essential care. In healthcare settings, the influence of ghairat often becomes starkly visible. …

Charitable interpretation in technically complex domains depends on accurately characterizing the object of interpretation. This article develops a domain-competence condition for evaluating arguments in artificial intelligence (AI) ethics. Domain competence is question-relative rather than synonymous with engineering expertise: sufficient competence requires understanding or verifying the technical and contextual premises that could materially alter a normative conclusion. We propose a counterfactual materiality test to determine that threshold. Functional understanding may be sufficient for many first-order judgments, but questions concerning responsibility, predictability, mitigation, agency, cultural validity, or system design may require deeper attention to mechanisms, model and data provenance, and deployment context. We connect this account to Davidsonian charity and Williamson’s knowledge-maximization proposal, arguing that both presuppose adequate epistemic access to the subject matter. We then develop epistemic interdependence as a practical model of premise tracing, competence matching, verification, and revision. The result is a non-technocratic account of when technical and contextual understanding becomes constitutive of justified ethical analysis in AI.

To better determine the history of modern birds, we performed a genome-scale phylogenetic analysis of 48 species representing all orders of Neoaves using phylogenomic methods created to handle genome-scale data.We recovered a highly resolved tree that confirms previously controversial sister or close relationships.We identified the first divergence in Neoaves, two groups we named Passerea and Columbea, representing independent lineages of diverse and convergently evolved land and water bird species. Among Passerea, we infer the common ancestor of core landbirds to have been an apex predator and confirm independent gains of vocal learning. Among Columbea, we identify pigeons and flamingoes as belonging to sister clades. Even with whole genomes, some of the earliest branches in Neoaves proved challenging to resolve, which was best explained by massive protein-coding sequence convergence and high levels of incomplete lineage sorting that occurred during a rapid radiation after the Cretaceous-Paleogene mass extinction event about 66 million years ago.

The problem is a traditional Chinese temperament is extremely important issue, the problem in different stages of development of the academic performance of different forms, the article is mainly from the development of this problem starting in the Song Dynasty, Zhu Xi and then examines the efforts of scholars on the benevolence and Hunan Love relationship to the discussion, which reveals the importance of temperament and Taoist internal shunt: the Ikawa, under the represented school of Zhu Xi, the advocates work only under the Han Yi Munetomo learning, and ease up on them; are those who as for enlightenment, as the representative of the Wufeng up camp, just work on the idea of the Shih-Jen, learning under the school's self-denial inherent within it. This Taoist internal differences largely based on the different nature to grasp the problem, the article seeks to explore how this difference affects the diversion of Taoism. I think, what is crucially important and widely neglected in the research of traditional Chinese philosophy is the problem concerning about the relationship between these two notion: "Xing" and "Qing" . It could be discerned as a pivotal clue in different stages in the development of traditional Chinese philosophy. I would like to point out how it has manifested in the movement of Neo-Confucianism in Song dynasty, which has caused different branches of this philosophical movement. And I think it would be clear to understand their different interpretations of this problem, if we would pay attention to the disputation between Chu Hsi and Nan-hsuan on the distinction between the two notions "Ren" and "Ai" . The opposite standpoints in their treatment of these two notions "Ren" and "Ai" has revealed the different comprehensions of the fundamental problem about "Xing" and "Qing" between these two schools of Neo-Confucianism. The school represented by Chu Hsi emphasized the necessity to cultivate and discipline oneself in practice so as to able to approach the inner Goodness, that is, to "Shi Ren" . But the opposite perspective which was taken by "Hu-hsiang" scholars, especially that of Nan-hsuan, claimed that it is only through the comprehension of the Goodness, that is, only one has the ability to grasp the Goodness immediately, that he would do appropriately in practical life and thus it would be natural for him to obtain the practial wisdom or scrutiny and self-discipline which was heavily stressed by Chu Hsi. I hope that through my interpretation of the debate aroused by different perspectives about the distinction between " Ren "and" Ai "in Neo-Confucianism and the consequences caused by it, one would get the impression of the significance of the problem of" Xing "and" Qing "in the development of traditional Chinese philosophy

This paper introduces the concept of AI-inclusive epistemology, suggesting that artificial intelligence (AI) may develop its own epistemological perspectives, function as an epistemic agent, and assume the role of a quasi-member of society. We explore the unique capabilities of advanced AI systems and their potential to provide distinct insights within knowledge systems traditionally dominated by human cognition. Additionally, the paper proposes a framework for a sustainable symbiotic society where AI and human intelligences collaborate to enhance the breadth and depth of epistemological pursuits. Through this exploration, we aim to redefine conventional knowledge paradigms by integrating the AI perspective, thereby fostering a more comprehensive and dynamic epistemological landscape.

In the expanding discourse on AI alignment and ethics, interdisciplinarity and cultural inclusivity are often treated as intrinsic goods. This has led to a proliferation of efforts connecting artificial intelligence with diverse cultural, religious, and philosophical frameworks. While such engagements can offer valuable perspectives, they also risk producing epistemic distortions when pursued without a clear rationale or contextual necessity. This paper identifies two such distortions: forced relevance and coerced inclusion. It first interrogates the growing tendency to draw connections between AI and external frameworks—such as theology, feminism, or postcolonial theory—not because a specific ethical or technical challenge demands them, but because these discourses are academically available or currently in vogue. When interdisciplinarity is treated as an end in itself, it risks instrumentalizing both AI and the traditions it draws upon, obscuring the purposes of each. Second, the paper challenges the assumption that all communities seek or ought to seek participation in global AI ethics discourse. Ethical reasoning in many traditions may be expressed through silence, ritual, or non-textual forms that do not conform to dominant expectations of discursive inclusion. When legitimacy is linked to visibility, these epistemologies are often sidelined. To address these concerns, the paper introduces the concept of epistemic modesty, a principled stance that respects irrelevance, dignifies refusal, and accepts non-participation as a legitimate ethical position. Rather than rejecting cultural engagement, it advocates for a more thoughtful and pluralistic approach to cross-cultural AI, one that listens carefully, engages selectively, and upholds the right not to align.

As the scope of innovative technologies is expanding, their implications and applications are increasingly intersecting with various facets of society, including the deeply rooted traditions of religion. This paper embarks on an exploratory journey to bridge the perceived divide between advancements in technology and faith, aiming to catalyze a dialogue between the religious and scientific communities. The former often views technological progress through a lens of conflict rather than compatibility. By utilizing a technology-centric perspective, we draw metaphorical parallels between the functionalities of new technologies and some theological concepts of Islam. The purpose is not to reinterpret religious concepts but to illustrate how these two domains can coexist harmoniously. This comparative analysis serves as a conversation starter with an intention to mitigate any apprehensions towards technology by highlighting its potential to align with religious concepts. By fostering an environment where technological innovations are seen as tools for enhancement rather than threats to tradition, we contribute to a more inclusive discourse that encourages the religious community to engage with and potentially embrace contemporary technological advancements.

The Urdu and Pashto term ghairat is often translated into English as ‘honour’, but this translation fails to capture its full ethical depth and cultural specificity. In South Asian and Muslim-majority societies, ghairat refers not simply to an individual’s reputation but to a deeply embedded, gendered moral code of vigilance, modesty and communal dignity. Even Pakistan’s Supreme Court has remarked that ghairat lacks a precise English equivalent, noting that in some contexts, it carries a connotation closer to ‘arrogance’ than ‘honour’.1 Western discourse is most familiar with ghairat through media coverage of ‘honor killings’, where women are killed by family members for perceived moral lapses. While such acts are undeniably brutal and ethically indefensible, they reflect a distorted and extreme manifestation of ghairat. The concept itself operates much more widely and subtly in everyday decisions, shaping family roles, gender norms and even access to healthcare. In its idealised form, ghairat embodies dignity and a sense of moral responsibility to protect family and community integrity. In its harmful form, it can become a mechanism of control and oppression, especially for women, limiting autonomy and delaying or denying essential care. In healthcare settings, the influence of ghairat often becomes starkly visible. …

In the past few years, many students have begun to lose interest in science and information and engineering technology courses because they find them too boring and hard to learn. To strengthen this field of education and stimulate students? motivation and interest in learning, this study introduces a theoretical pedagogical framework based on cooperative learning theory and tailored to the realities of the university education system in China. In the framework, a group in a class is treated as a system that goes through different stages and is subject to different input instructions and output outcomes. Based on this structure, teachers? interventions serve as feedback for controlling inputs to promote the correct operation of the system. This study aims to promote the development of a cooperative learning model for university students. A case study of the application of the framework shows that it has positive and active effects on student learning

This paper addresses how people understand Explainable Artificial Intelligence (XAI) in three ways: contrastive, functional, and transparent. We discuss the unique aspects and challenges of each and emphasize improving current XAI understanding frameworks. The Ritual Dialog Framework (RDF) is introduced as a solution for better dialog between AI creators and users, blending anthropological insights with current acceptance challenges. RDF focuses on building trust and a user-centered approach in XAI. By undertaking such an initiative, we aim to foster a thorough Understanding of XAI, capable of resolving the current issues of acceptance and recognition.

Editor's AbstractIn one of the very first reactions to Li Minghui’s criticism of Mainland New Confucianism (MNC), Zeng Yi emphasizes the ties between MNC and Han-dynasty “Classical Learning” (jingxue), as opposed to the basis of Mou Zongsan-style New Confucianism in Song-dynasty Neo-Confucian “Way learning” (Daoxue). He further connects the MNC approach with an institutional, “concrete continuation” of the Confucian tradition, as opposed to the abstract, philosophical approach of Mou Zongsan.This short essay, another of the immediate reactions to Li Minghui's criticism of Mainland New Confucianism, focuses on the distinction between “old” (or traditional) and “new” Confucianism, and their differing relations to liberal democracy. Fang identifies a tension in Li Minghui's attitude toward the “old,” since Li seems to want to have a connection with the tradition but also not to be bound by it.

The prevailing discourse comparing Artificial Intelligence (AI) and Natural Intelligence (NI) overlooks a critical dimension: culture. Intelligence, whether artificial or human, cannot be fully understood without recognizing its cultural foundations and adaptive contexts. This Perspective introduces two key contributions. First, it defines the Cultural Cognition Gap, the disconnect between AI’s static, pattern-based reasoning and the dynamic, culturally adaptive nature of human cognition, evident in real-world deployment failures. Second, it proposes Culture Driven AI, a conceptual and normative framework advocating for AI systems designed to engage with cultural plurality and fluidity as central features of intelligent behavior. By situating intelligence within its cultural contexts, this work highlights the limitations of current AI design and calls for interdisciplinary collaboration to develop systems that are not only technically aligned but also culturally attuned, ethically grounded, and responsive to the diverse realities of human societies.

Since the latter half of the twentieth century, science fiction narratives centered on humanoid robots have continuously explored the future of human–machine symbiosis through embodied character design, providing a conceptual testing ground for real-world robotic development. This study focuses on the metaphorical mechanisms underlying the “quasi-body” of robots in such narratives, revealing how they challenge the stability of the human concept. By analyzing how various humanoid robotic figures in science fiction narratives are modeled upon the human “body” and how the robots’ “quasi-body” reciprocally reshape the concept of the “human”, this article employs a dual perspective integrating phenomenological embodiment theory and conceptual metaphor theory. The argument unfolds in three progressive stages: deconstructing the metaphorical imitation of human in robotic embodiment within science fiction narrative, critiquing the simplified functional body that overlooks the fundamental role of the body in cognition, and tracing the reverse influence of humanoid metaphor modeling on the conceptualization of the human. This study seeks to expose the intrinsic tensions embedded in bodily metaphorization within human–robot modeling. As human–robot/machine symbiosis becomes an increasingly normalized condition of existence, only by disrupting entrenched cognitive frameworks of body stereotype can we cultivate novel relational paradigms imbued with greater ethical imagination in the technological reality.

The growing momentum to introduce artificial intelligence (AI) education into early schooling raises critical concerns about ethical readiness. Teaching technical skills in the absence of moral development risks producing a generation ill-equipped to navigate the profound societal consequences of AI. In response to this challenge, we introduce the moral grounding model (MGM), a values-first model of education that prioritizes ethics, cultural awareness, and relational thinking before technical instruction. To support this model, we examine a range of Eastern educational traditions, demonstrating how moral and philosophical training historically preceded specialization. We further connect these traditions to contemporary AI ethics principles such as fairness, accountability, and safety, to demonstrate how foundational moral education can inform the ethical development, design, and governance of AI systems. The paper concludes by advocating a values-first curriculum that cultivates future technologists who are not only capable but ethically conscious and socially responsible.

Background In this paper, we explore the question “Why can’t AI be a coauthor?” and reveal a rarely discussed reason. Methods and Results First, allowing AI to be a coauthor disregards the uniquely human experience of writing texts. This means that human authors are seen as mere producers of texts rather than rational beings engaged in a value-added and humanized learning process expressed through the paper. The relationship between the human author and the thesis is reduced to a mere result of generation rather than a result of individual human critical thinking. Second, allowing AI to be a coauthor leads to self-delusion about one’s own rationality and thus violates the responsibility to understand the world correctly. In this process of self-deception, it is not as if those who grant AI coauthor status do not realize that AI is not the same as humans; however, they self-deceivingly assume that AI has the same internal state as humans. This means that the relationship between the author and the work is no longer seen as a position to be respected, but as something probabilistic and gamified. Conclusions Finally, we discuss the potential consequences of these rationales, concluding that including AI as a coauthor implies a disregard for humanization.

Background: Disclosure of AI use is seen as a sign of the author’s honesty and commitment to the principle of transparency. However, existing discussions have paid little attention to a special case: authors who honestly disclose their use of AI feel ashamed because of their honesty. Methods and Results: The main issue discussed in this paper is why authors experience shame in the process of responsible disclosure of AI use. We redefine this emotion and its causes from the perspective of moral emotions. We argue that current disclosure policies only emphasize honesty but do not address how this honesty should be fairly treated. Conclusions: Current disclosure guidelines should ensure that authors feel more secure when disclosing AI use honestly in academic papers, thereby promoting an effective and responsible culture of disclosure. This requires more constructive narrative support. Expressing appreciation and respect for the honesty represented by disclosure is an appropriate way to address the issues discussed in this paper.

Artificial intelligence (AI) is increasingly integrated into healthcare systems, offering advances in diagnosis, treatment, and operational efficiency. However, the cultural assumptions embedded in AI training data often misalign with the sociocultural realities of diverse patient populations—a phenomenon we term the Cultural Calibration Gap (CCG). This conceptual paper defines the CCG as a structural and epistemic deficit, distinct from algorithmic bias or dataset shift, that undermines clinical validity, patient trust, and health equity. We analyze how culturally uncalibrated AI systems exacerbate disparities in diagnosis and care, compromise informed consent, and erode trust, particularly in communities with collective, spiritual, or traditional health frameworks. To address the CCG, we propose a three-pronged strategy: epistemic inclusion of diverse knowledge systems and data sources, participatory design grounded in community-based research, and regulatory reform mandating cultural responsiveness in model validation and oversight. We argue that tackling the CCG is not merely an ethical imperative but a scientific necessity, aligning healthcare AI with the evolving vision of human-centric, adaptive Healthcare 5.0. Culturally calibrated AI systems promise not only improved precision and trustworthiness but also more equitable and contextually meaningful care across global health landscapes.

Jecker et al critically analysed the predominant focus on existential risk (X-Risk) in artificial intelligence (AI) ethics, advocating for a balanced communication of AI’s risks and benefits and urging serious consideration of other urgent ethical issues alongside X-Risk.1 Building on this analysis, we argue for the necessity of acknowledging the unique attention-grabbing attributes of X-Risk and leveraging these traits to foster a comprehensive focus on AI ethics. First, we need to consider a discontinuous situation that is overlooked in the article by Jecker et al. This discontinuity refers to the phenomenon where X-Risk is perceived as dominating the discourse, yet contrary to expectations, it does not lead to a significant allocation of social resources for specific risk management and practical initiatives. In both the specific realm of ethical AI initiatives and the broader scope of AI risk management, the responses to X-Risk do not have the advantage of prioritising the allocation of resources over other related risks.2 This discrepancy suggests that, in terms of actual social resource allocation, X-Risks do not receive commensurate resources relative to the attention they attract. Unlike other types of risks, X-Risk is perceived as a distant threat, which does not correspond with actual initiatives in terms of media exposure. Despite the prominence of the longtermism view in various media and public discourse, the X-Risk of AI often serves merely as a cautionary note or opinion about the current situation. This suggests that concerns about an AI-driven catastrophe have not been effectively translated into practical initiatives. The gap between the attention drawn and …

This paper puts forth Central Asian AI ethics principles and proposes a layered strategy tailored for the development of ethical principles in the field of artificial intelligence (AI) in Central Asian countries. This approach includes the customization of AI ethics principles to resonate with local nuances, the formulation of national and regional-level AI ethics principles, and the implementation of sector-specific principles. While countering the narrative of ineffectiveness of the AI ethics principles, this paper underscores the importance of stakeholder collaboration, provides a comprehensive framework, and emphasizes the need for responsible AI practices. By adopting this approach, Central Asian region can contribute towards the regional integration and global discourse on AI ethics while promoting the responsible use of AI technology in their respective countries.

“Deathbots,” digital constructs that emulate the conversational patterns, demeanor, and knowledge of deceased individuals. Earlier moral discussions about deathbots centered on the dignity and autonomy of the deceased. This paper primarily examines the potential psychological and emotional dependencies that users might develop towards deathbots, considering approaches to prevent problematic dependence through temporary use. We adopt a hermeneutic method to argue that deathbots, as they currently exist, are unlikely to provide substantial comfort. Lacking the capacity to bear emotional burdens, they fall short of meeting idealistic expectations. By repositioning deathbots, we aim to mitigate the risk of emotional dependency and respect the natural grieving process. Our goal is to propose the use of deathbots as a novel means of mourning through transitory use, rather than as a method to alleviate grief or as a patterns for communication with the deceased.

Achieving the global benefits of artificial intelligence (AI) will require international cooperation on many areas of governance and ethical standards, while allowing for diverse cultural perspectives and priorities. There are many barriers to achieving this at present, including mistrust between cultures, and more practical challenges of coordinating across different locations. This paper focuses particularly on barriers to cooperation between Europe and North America on the one hand and East Asia on the other, as regions which currently have an outsized impact on the development of AI ethics and governance. We suggest that there is reason to be optimistic about achieving greater cross-cultural cooperation on AI ethics and governance. We argue that misunderstandings between cultures and regions play a more important role in undermining cross-cultural trust, relative to fundamental disagreements, than is often supposed. Even where fundamental differences exist, these may not necessarily prevent productive cross-cultural cooperation, for two reasons: (1) cooperation does not require achieving agreement on principles and standards for all areas of AI; and (2) it is sometimes possible to reach agreement on practical issues despite disagreement on more abstract values or principles. We believe that academia has a key role to play in promoting cross-cultural cooperation on AI ethics and governance, by building greater mutual understanding, and clarifying where different forms of agreement will be both necessary and possible. We make a number of recommendations for practical steps and initiatives, including translation and multilingual publication of key documents, researcher exchange programmes, and development of research agendas on cross-cultural topics.

This paper presents social data and knowledge collective intelligence platform for TRaining Ethical AI Models (STREAM) to address the challenge of aligning AI models with human moral values, and to provide ethics datasets and knowledge bases to help promote AI models “follow good advice as naturally as a stream follows its course”. By creating a comprehensive and representative platform that accurately mirrors the moral judgments of diverse groups including humans and AIs, we hope to effectively portray cultural and group variations, and capture the dynamic evolution of moral judgments over time, which in turn will facilitate the Establishment, Evaluation, Embedding, Embodiment, Ensemble, and Evolvement (6Es) of the moral capabilities of AI models. Currently, STREAM has already furnished a comprehensive collection of ethical scenarios, and amassed substantial moral judgment data annotated by volunteers and various popular Large Language Models, collectively portraying the moral preferences and performances of both humans and AIs across a range of moral contexts. This paper will outline the current structure and construction of STREAM, explore its potential applications, and discuss its future prospects.