"Ignorance, Opacity, and Dependence: Epistemic Challenges for Scientists in the Age of AI"

This workshop aims to explore how AI reshapes the scientists' epistemic environment. What kinds of ignorance does AI mitigate, and what new forms does it generate? How should we conceptualize epistemic dependence on complex computational systems? Can scientific understanding survive—or even thrive—under conditions of opacity?

We welcome contributions from philosophy of science, epistemology, philosophy of AI, logic, and related disciplines addressing (but not limited to) the following questions:

• Does reliance on AI systems undermine or transform scientific understanding?
• What is the epistemic status of results produced by opaque or non-interpretable models?
• How should we conceptualize epistemic dependence in AI-mediated research?
• Are new norms of justification required in computationally intensive sciences?
• How should we understand epistemic trust in the context of proprietary (usually corporate-controlled) AI systems?
• How does AI-mediated research reshape the division of epistemic labor within scientific communities? 
• Does the use of AI systems shift the epistemic agency from individual scientists to distributed (human-AI) collectives? 
• Can ignorance generated by AI systems be epistemically productive?
• How should responsibility and accountability be distributed in AI-supported research?
• What role should transparency, interpretability, and explainability play in scientific practice?
• Do AI-driven methods challenge traditional distinctions between data, models, and theories?