The 2nd Workshop on Epistemic Intelligence in Machine Learning: Learning under Unknown Unknowns for Real-world Impact

Machine learning systems are increasingly deployed in open-ended and high-stakes environments, where distribution shift, adversarial manipulation, hallucinations, safety risks, and misalignment reveal fundamental limits of learning under incomplete information. A central challenge is the ability to recognise and reason about the limits of one’s own knowledge, especially in the presence of unknown unknowns. The 2nd Workshop on Epistemic Intelligence in Machine Learning brings together researchers from diverse areas of machine learning to develop principled and computationally tractable approaches to representing and operationalising epistemic intelligence. The workshop focuses on foundations of uncertainty beyond single-distribution representations, uncertainty-aware generative and foundation models, AI safety and alignment under objective uncertainty, and lifelong and continual learning in open worlds. By connecting theoretical frameworks with behavioural mechanisms such as abstention, deferral, querying, and safe adaptation, EIML aims to provide a unifying perspective on how learning systems can reason under unknown unknowns and guide robust, safe, and trustworthy real-world deployment.