AI Pluralism and the Worlds It Misses
arXiv:2606. 16167v1 Announce Type: new Abstract: AI pluralism is often framed as a problem of representing diverse values, preferences, users, or outputs.
arXiv:2606. 07802v1 Announce Type: cross Abstract: Culture is the most insidious vector of gradual human disempowerment by AI: unlike economic or political displacement, cultural displacement attacks the very preferences and values through which humans recognise and resist disempowerment itself.
arXiv:2606. 16167v1 Announce Type: new Abstract: AI pluralism is often framed as a problem of representing diverse values, preferences, users, or outputs.
arXiv:2606. 13755v1 Announce Type: cross Abstract: We argue that aligning AI to aggregated human preferences is the wrong target.
arXiv:2607. 02955v1 Announce Type: cross Abstract: We argue that AI systems used in conducting foreign policy tasks - broadly enacting 'statecraft' - should be a priority test case for technical AI governance research.
The paper titled "Lost in Translation: How Universal Ethical Values Fail to Translate Across Global Contexts" examines how AI ethics frameworks that assume values such as fairness, transparency, and accountability are universal actually differ in practice. By interviewing 14 experts from 10 countries, the authors find that these values are reinterpreted to fit local moral logics—privacy becomes collective, transparency becomes trust‑building, and fairness becomes equity in access—revealing translation gaps between global frameworks and local practices. The study proposes plural governance pathways that redistribute epistemic authority and treat ethical negotiation as an ongoing, context‑sensitive process.
arXiv:2606. 07536v1 Announce Type: cross Abstract: Frontier artificial intelligence is reshaping all aspects of society, from economic output or military capability to democratic institutions.
arXiv:2607. 14782v1 Announce Type: new Abstract: Grounded in human rights-based frameworks such as the UNESCO Recommendation on the Ethics of AI, the Global Index on Responsible AI (GIRAI) examines how countries translate responsible AI commitments into enforceable protections, institutional capacity, and redress mechanisms.
The essay reviews Matthijs Maas’s framework for global AI governance, highlighting the rapid, border‑less development of AI and the fragmented, non‑binding international responses. It argues that governance cannot rely on a single institutional blueprint but must account for the varied powers of states, international bodies, and private firms. The central challenge is shaping an evolving architecture amid actors with differing incentives and no shared plans.
arXiv:2608. 11006v1 Announce Type: cross Abstract: Governments worldwide have responded to the rapid expansion of AI by publishing national and regional AI strategies.
arXiv:2606. 01417v1 Announce Type: new Abstract: Turkey's e-Government Gateway (e-Devlet) serves over 68 million registered users with more than 9,200 government services, and is increasingly integrating artificial intelligence into citizen-facing applications such as chatbot assistants and eligibility assessments.
arXiv:2608. 16470v1 Announce Type: cross Abstract: We examine the worldwide trend of mandatory labeling of generative artificial intelligence(GenAI) as a reactive, symbolic form of legislation triggered by technological panic and institutional responses.
Governments worldwide have responded to the rapid expansion of AI by publishing national and regional AI strategies. Comparing national and regional AI strategies to identify their convergences and divergences can uncover their common practices, understand regional variations, and provide policy designers a comprehensive set of policy design elements for their ongoing AI strategy developments.
The paper proposes a new interdisciplinary field called Cognitive Infrastructure Studies (CIS) to examine how AI systems act as invisible, foundational cognitive infrastructures that shape what people can know and do in digital societies. It argues that these infrastructures, through anticipatory personalization and adaptive invisibility, automate relevance judgments and shift epistemic agency to non‑human systems. CIS offers methodological tools, such as infrastructure breakdown experiments, to uncover the hidden cognitive dependencies created by AI preprocessing across individual, collective, and societal levels.