Jul 2026· AI and Ethics· Vol 6· 0 citations· 54 references
Computer Science
TL;DR
The Qualitative Model of Ethics (QME), a naturalistic and teleological framework grounded in a single evaluative scalar: the generative capacity of the Whole Living System (WLS), provides a unified and operational framework for AI governance at scale.
A Multi-Layer Social-Theoretical AI Ethics Framework (MLST-AEF) that integrates normative ethical reasoning, stakeholder analysis, institutional context, bias and power assessment, and structured decision support is developed.
M. Fakrudeen, J. Otieno· AI and Ethics· 0 citations
A novel methodology for future-proofing global operations against institutional voids and ethical risks in the digital infosphere is provides a novel methodology for future-proofing global operations against institutional voids and ethical risks in the digital infosphere.
Martin Sposato, Eduardo Carlos Dittmar· Journal of Information, Comm...· 0 citations
The increasing deployment of autonomous, agentic AI systems challenges traditional accountability mechanisms. Existing research predominantly frames AI accountability gaps as barriers that can be overcome through better standards, transparency, and institutional reform. We argue that this framing is insufficient: certain configurations of actors, systems, and institutions render AI accountability conceptually unachievable regardless of effort. We introduce the concept of constitutive AI unaccountability to capture these configurations. Through a three-stage qualitative study comprising a concept-centric literature analysis, a secondary analysis of 27 expert interviews with AI professionals from technical, legal, and sociotechnical backgrounds, and an illustrative framework application to the open-source agentic AI system OpenClaw, we identify nine categories and 20 themes of constitutive AI unaccountability. These are organized across structural, technological, and normative clusters and reinforce one another through eight directed interdependencies. Our framework is operationalized as a diagnostic instrument of 20 questions, which detected 17 of 20 conditions when applied to OpenClaw, including an inverted anthropomorphism configuration in which the AI agent was the only identifiable actor. We contribute a reframing of AI unaccountability as a constitutive property of sociotechnical systems, an extension of the four barriers to accountability, and a practical instrument for identifying accountability voids in specific AI deployments.
L. H. Nguyen, E. Späthe, S. Lins et al.· 0 citations
Large language model (LLM)-based applications are becoming increasingly integrated into everyday practices of communication, learning, and creativity. Their widespread adoption has intensified debates in AI ethics concerning how their societal significance should be understood and evaluated. Existing approaches to AI ethics have developed important concepts and governance frameworks for evaluating the consequences of AI systems, particularly in relation to their design, deployment, and identifiable impacts. These approaches have proven indispensable for analyzing harms, assigning responsibility, and guiding governance. However, the widespread incorporation of LLMs into everyday practices also raises questions about more gradual and cumulative transformations that emerge through repeated human–LLM interaction. Drawing on John Dewey’s pragmatist account of inquiry, habit formation, and moral reconstruction, we distinguish between consequences, identifiable outcomes that can be evaluated within existing normative and regulatory frameworks, and effects, the cumulative transformations of the habits and conditions through which inquiry, judgment, and action are organized over time. We argue that interactions with LLM-based applications give rise to both consequences and effects, and that these should be understood as complementary rather than competing analytical perspectives. Building on this distinction, we use Dewey’s conception of habits to examine how repeated engagement with LLMs may contribute to transformations in three domains: epistemic inquiry, affective-relational self-understanding, and cultural meaning-making. Rather than proposing an alternative to existing AI ethics, we argue that attending to effects complements current approaches by directing ethical inquiry towards the cumulative transformations that emerge through everyday human–LLM interaction.
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.
Debates on artificial intelligence (AI) governance focus on legal regulation, risk management, institutional accountability, and the division of responsibilities between the state, market, and other social actors. However, the anthropological and social assumptions underlying governance models remain underexplored, particularly in relation to the risk of erasing human dignity amid the growing technocratisation and algorithmisation of social life. The study analyses Leo XIV’s reflections on AI governance. This article adopts a conceptual and comparative approach. It is based on a close reading of the encyclical Magnifica Humanitas and a systematic comparison of the framework for AI governance articulated in the document with dominant AI governance models identified in the literature. This study reconstructs an implicit hybrid model of AI governance grounded in eight principles: (1) human dignity, (2) the common good, (3) the universal destination of goods, (4) social justice, (5) solidarity, (6) subsidiarity, (7) shared responsibility, and (8) transparency/accountability. The Tower of Babel – Nehemiah heuristic demonstrates that contemporary AI governance can be understood as a fundamental tension between two logics: one oriented toward centralisation, control, and exclusion, and another toward participation, distributed responsibility, and the protection of human dignity. As an additional contribution, the study operationalises the proposed model through the example of AI-assisted allocation of social benefits, translating its normative principles into concrete governance requirements. Leo XIV’s reflections not only align with a hybrid governance model but provide a normative corrective to both state-led and market-led models by prioritising relational human dignity and shared responsibility over centralised control. The study develops a Leo XIV-informed, normatively grounded hybrid model of AI governance that extends beyond the exercise of agency over technology to fostering a more just and inclusive social order in which technology enables human flourishing.
S. Fel, Marta Choroszewicz, Jarosław Kozak· AI and Ethics· 0 citations