Yi Zeng

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 …

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.

Humans often unconsciously perceive social robots involved in their lives as partners rather than mere tools, imbuing them with qualities of companionship. This anthropomorphization can lead to a spectrum of emotional risks, such as deception, disappointment, and reverse manipulation, that existing approaches struggle to address effectively. In this paper, we argue that a Virtual Interactive Environment (VIE) exists between humans and social robots, which plays a crucial role and demands necessary consideration and clarification in order to mitigate potential emotional risks. By analyzing the relational nature of human-social robot interaction, we discuss the connotation of such a virtual interactive environment that is similar to the emotional states aroused when reading novels. Building on this comprehension, we further demonstrate that manufacturers should carry out comprehensive Virtual Interactive Environment Indication (VIEI) measures during human-social robot interaction with a stricter sense of responsibility when applying social robots. Finally, we contemplate the potential contributions of virtual interactive environment indication to existing robot ethics guidelines.

Firstly, the "Metaverse" possesses two distinctive features, "thickness" and "imagination," promising the public a structure of unknown scenarios but with unclear definitions. Attempts to establish an open framework through incompleteness, however, fail to facilitate interactions between humans and the scenario. Due to the dilemma of "digital twinning," the "Metaverse" cannot be realized as "another universe". Hence, the "Metaverse" is, in fact, only a virtual experiential territory created by aggregating technologies that offer immersion and interactivity. Secondly, when artificial intelligence serves as a key supporting technology for the "Metaverse," it presents multifaceted existential risks, manifested as safety, cognitive, and ethical dilemmas. Finally, we propose that the framework for responsible "Metaverse" governance should be constructed through three pathways: collective responsibility, realism, and the co-governance of the virtual and real.

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.

The ongoing evolution of advanced AI systems will have profound, enduring, and significant impacts on human existence that must not be overlooked. These impacts range from empowering humanity to achieve unprecedented transcendence to potentially causing catastrophic threats to our existence. To proactively and preventively mitigate these potential threats, it is crucial to establish clear redlines to prevent AI-induced existential risks by constraining and regulating advanced AI and their related AI actors. This paper explores different concepts of AI existential risk, connects the enactment of AI red lines to broader efforts addressing AI's impacts, constructs a theoretical framework for analyzing the direct impacts of AI existential risk, and upon that proposes a set of exemplary AI red lines. By contemplating AI existential risks and formulating these red lines, we aim to foster a deeper and systematic understanding of the potential dangers associated with advanced AI and the importance of proactive risk management. We hope this work will contribute to the strengthening and refinement of a comprehensive AI redline system for preventing humanity from AI existential risks.

This paper explores the potential of integrating ancient educational principles from diverse eastern cultures into modern AI ethics curricula. It draws on the rich educational traditions of ancient China, India, Arabia, Persia, Japan, Tibet, Mongolia, and Korea, highlighting their emphasis on philosophy, ethics, holistic development, and critical thinking. By examining these historical educational systems, the paper establishes a correlation with modern AI ethics principles, advocating for the inclusion of these ancient teachings in current AI development and education. The proposed integration aims to provide a comprehensive education that not only encompasses foundational knowledge but also advanced learning, thereby equipping future AI professionals with the necessary tools to develop AI systems that are ethically responsible, culturally aware, and aligned with human values such as fairness, safety, transparency, and collaboration. This approach not only addresses the AI alignment problem but also fosters cultural harmony and global understanding, which are crucial in an increasingly interconnected world. The paper posits that the wisdom of ancient educational systems, when harmonized with modern AI ethics, can guide the development of AI technologies that are beneficial for humanity, ensuring these advancements are not just technologically sound but also ethically and culturally informed.