arXiv AI

Epistemic Trustworthiness in Generative AI: A Normative Framework for Warranted Reliance in High-Stakes Workflows

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 AI
Jul 1

Who Determines the Meaning of an Emotion? Affective Sovereignty as an Epistemic Consequence of Measurement Limits

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.

By Keito Inoshita
arXiv AI
Jul 29

Beyond Epistemia: Epistemic Schizologia and Large Language Models as Techno-Semiotic Machines

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.

By Federico Cabitza, Gianluca Colombo
arXiv AI
2d ago

Never the Number: Structural Abstention for AI Systems Whose Answers Are Consumed as Fact

arXiv:2608. 13926v1 Announce Type: new Abstract: Large language models have made natural language interfaces to databases (NLIDB) newly credible, but LLM text-to-SQL systems fail in a way that matters for deployment: a hallucinated column or a mis-aggregated total yields a fluent wrong answer, indistinguishable at the point of use from a right one.

By Zhelun (Allen), Wu
arXiv AI
Jun 11

The Impossibility of Eliciting Latent Knowledge

arXiv:2606. 12268v1 Announce Type: new Abstract: Advanced AI systems have extensive knowledge of their environments; in fact, their knowledge may (far) exceed that of their developers or users.

By Korbinian Friedl, Francis Rhys Ward, Paul Yushin Rapoport, Tom Everitt, Jonathan Richens