The paper argues that this trend warrants a re-examination of Verbeek’s framework for its capacity to explain the co-evolution of technology and morality in the digital age, and it envisions a future of human–machine value co-evolution organized around new research directions such as “Setting as Governance” and “value homeostasis mechanisms”.
Abstract
This study conducts a technical analysis of frontier generative AI algorithms—including Meta’s Self-Rewarding Language Models, DeepMind’s EVA (Evolving Alignment via Asymmetric Self-Play) framework, and DeepSeek’s pure reinforcement-learning models—in order to examine an intrinsic paradigm shift in the ethical governance of generative artificial intelligence and to advance a physicalist analysis of algorithmic endogenous ethics. Combining a close reading of alignment techniques (RLHF, DPO, iterative DPO, GRPO) with a conceptual analysis grounded in Peter-Paul Verbeek’s theory of technological mediation and moral materialization, the paper traces how value-alignment goals are being “materialized” into internal, dynamic, and evolvable “moral scripts” within the algorithms themselves. The analysis shows that contemporary alignment practices are moving from external ethical discipline toward endogenous norms generated through iterative self-evaluation, asymmetric self-play, and rule-based self-exploration. The paper argues that this trend warrants a re-examination of Verbeek’s framework for its capacity to explain the co-evolution of technology and morality in the digital age, and it envisions a future of human–machine value co-evolution organized around new research directions such as “Setting as Governance” and “value homeostasis mechanisms”.
The argument further holds that AI does not possess moral agency in the classical sense but functions as an infrastructural precondition for the reconfiguration of normative hierarchies—in an empirical rather than transcendental sense.
The paper’s central argumentative shift is to change the narrative from bias mitigation to bias management—treating bias not as a defect to be corrected but as an ongoing condition to be governed.
Gabriela Arriagada-Bruneau· Science and Engineering Ethi...· 0 citations
The development of generative artificial intelligence (AI) has introduced novel risks, including deepfakes and algorithmic hallucinations, urgently demanding a fundamental shift in global AI ethics from theoretical presuppositions toward actionable governance practices. By uncovering the developmental trajectory of ethical discourse in the era of generative AI, this study conducts a systematic analysis of the existing literature, aiming to generate actionable insights for future interdisciplinary research and contribute to the reconstruction of a renewed ethical order in this field. To this end, this study constructs a mixed-methods framework integrating macro-level bibliometrics with the micro-level BERTopic deep semantic mining approach. Following a structured multi-stage screening protocol, this paper performs a quantitative analysis of 1,190 core documents (2020–2025) retrieved from the Web of Science and Scopus databases, identifying 10 core themes and examining their spatio-temporal evolution.The findings reveal that 2023 represents a pivotal turning point in generative AI ethics research ; by 2025, the human-centered theme of ‘education and cognitive literacy’ had surpassed the long-dominant topic of ‘law and governmental regulation’ in publication volume. This shift suggests a reorientation in global governance discourse from a defensive emphasis on ‘technical compliance’ toward an adaptive focus on ‘human-centered cognitive empowerment’. Furthermore, global knowledge production exhibits a pronounced “center-periphery” structure, where a small group of developed countries dominates agenda-setting, while the Global South remains in a marginal position ; meanwhile, the academic literature tends to cluster into two distinguishable discursive orientations: one centered on ‘technical governance’ and the other on ‘humanistic reflection’. This study calls for bridging the binary epistemological divide by integrating humanistic values into technological instrumental rationality, while broadening the current discourse framework and addressing structural imbalances through more inclusive North-South collaboration, with the ultimate goal of advancing global ‘epistemic justice’ in the era of generative AI.
Zhiqi Deng, Hong Zhang, Su Tao· Discover Artificial Intellig...· 0 citations
The rise of agentic AI systems, which are autonomous, proactive, and capable of multi-step task execution, has transformed how individuals interact with intelligent technologies. While these systems promise efficiency and enhanced decisionmaking, they also introduce new ethical vulnerabilities. This study investigates a paradoxical mechanism in AI-assisted academic task delegation: as social acceptance of AI delegation increases, individuals rely less on internal moral regulation. Drawing on Moral Disengagement Theory and Social Norms Theory, we test a normative substitution model using SEM data from 280 European university students and find that subjective norms function as both mediator and moderator, amplifying delegation intentions while reducing the influence of moral disengagement. Shame proneness emerges as a secondary moderator that buffers the normative pull for individuals with strong internal moral emotions. These findings highlight a critical socio-technical risk: proactive AI systems may unintentionally erode moral accountability as their use becomes socially normalized. We discuss implications for responsible agentic AI design, governance, and human-AI collaboration.
Yasser Al Helaly, A. Ashofteh· Annual International Compute...· 0 citations
It is argued that demonstrating internal incoherence is a necessary precursor to AI alignment as well as a broader phenomenon of epistemic instability in generative AI wherein models fail to reliably maintain coherence with respect to their own prior outputs.
Pegah Nokhiz, Aravinda Kanchana Ruwanpathirana, Helen Nissenbaum· 0 citations
Why do we form moral judgments? One influential answer is that the human capacity for moral judgment is an evolutionary adaptation. P. Kyle Stanford argues that morality is adaptive because it leads individuals to externalize norms, prompting them to evaluate both their own behavior and that of potential partners by a single standard. In this paper, we examine Stanford’s hypothesis using a game-theoretic model. Our results support and extend his proposal, demonstrating that – under the assumptions chosen – externalization is a plausible driver of the evolution of morality as it fosters the learning of cooperation in challenging novel contexts. We find that externalization allows agents to avoid the exploitation trap: by resisting unsustainable short-term gains through exploitation, externalizing agents secure the benefits of robust long-term cooperation. By modeling externalization as a constraint on strategy and partner choice, we avoid the common pitfall of game-theoretic approaches to represent morality as a mere disposition toward altruistic behavior.