arXiv:2608. 00961v2 Announce Type: replace-cross Abstract: AI anthropomorphism is typically treated as a problem of user misperception requiring institutional correction.
arXiv:2608. 05602v1 Announce Type: new Abstract: Generative AI systems are increasingly deployed in high-stakes professional contexts, where their outputs shape what users believe, how they reason, and what they treat as settled.
arXiv:2609.38486v1 Announce Type: cross Abstract: Large Language Models (LLMs) and more broadly Artificial Intelligence (AI) systems are often described and understood in human-like terms, a phenomen...
arXiv:2607. 25620v1 Announce Type: new Abstract: Quattrociocchi and colleagues warn that the fluent outputs of large language models may allow linguistic plausibility to substitute for epistemic evaluation, producing the condition they call *Epistemia*: the experience of possessing knowledge without undertaking the practices through which judgment would ordinarily be warranted.
arXiv:2607. 24391v1 Announce Type: cross Abstract: AI systems already govern.
arXiv:2606. 12713v1 Announce Type: new Abstract: Claims that artificial general intelligence has already arrived and claims that it remains decades away are often defended from overlapping evidence.
The article discusses Bryan Cantrill’s response to a tweet by former Anthropic employee Jacob Coxon, who claimed that AI could kill humanity by the end of the decade. Cantrill shares a personal anecdote about how his own youthful mistakes caused undue panic among non‑technical peers and warns against repeating that pattern. He emphasizes that domain experts must be cautious when making alarmist claims, especially about complex topics like critical infrastructure, bioweapons, and extinction, and that the burden of accurate information lies with those making such statements.
arXiv:2608. 08408v1 Announce Type: cross Abstract: Logics of abstraction in computational AI research often push important forms of knowledge and reflection aside: dominant standards of legitimacy separate from lived experience of harm; the goals of work misalign with the practices that operationalize them; and career demands crowd out critical reflection.
arXiv:2608.24748v1 Announce Type: cross Abstract: How can humans make sense of the rapid takeoff of artificial intelligence (AI)? We studied the sensemaking dynamics of AI through an open-ended, mixe...
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.
arXiv:2606. 31442v1 Announce Type: new Abstract: Emotion-sensing AI is rapidly becoming embedded in vehicles, home appliances, dialogue agents, and social infrastructure, giving rise to a sphere in which emotion is no longer confined to individual experience but is instead observed and computed at a societal scale, a domain we term the Affectosphere.
arXiv:2511. 08639v4 Announce Type: replace-cross Abstract: Existing AI disclosure mandates in scholarship require that AI assistance be reported but leave transparency philosophically unspecified: they fix the duty without explaining what the duty serves.
arXiv:2606. 05256v1 Announce Type: new Abstract: This study analyzes a publicly released dataset from a discontinued field experiment on Reddit's r/ChangeMyView.