Recent advances in artificial intelligence—especially large language models—have renewed foundational philosophical questions about language, inference, and knowledge. These systems produce linguistically well-formed outputs and display patterns of reasoning, yet their epistemic and semantic status remains unclear. Are we witnessing new forms of linguistic agency, or merely sophisticated statistical artifacts? And what kinds of logical and formal tools are adequate to capture their behavior?

This workshop aims to bring together perspectives from logic, linguistics, and philosophy to address these questions.

Epistemology of AI outputs: What is the epistemic status of AI-generated content? Can such outputs constitute knowledge, understanding, or evidence? Under what conditions, if any, are they reliable or trustworthy?
Language and meaning in AI: Do the outputs of large language models qualify as genuine speech acts? Can they be said to have meaning, reference, or intention, or are these merely ascriptions from the user’s perspective?
Logic and formal modeling: What logical frameworks are best suited to model AI behavior—classical, non-classical, probabilistic, or hybrid approaches? How should we formalize phenomena such as inconsistency, opacity, or context-sensitivity in AI systems?
Understanding and explanation: Do AI systems exhibit any form of understanding, or only simulate it? What would count as an explanation of their outputs, and how does this relate to broader debates on scientific understanding
Normativity and evaluation: What norms—epistemic, semantic, or pragmatic—should govern the use and assessment of AI outputs? Can traditional notions of justification and validity be extended to these systems?
Bias, gender, and injustice: How do AI systems reproduce or amplify existing social biases, including those related to gender? What forms of epistemic or linguistic injustice arise in their deployment, and how should they be addressed.

https://sites.google.com/view/from-logic-to-language-philai/home