Discovering Our Blind Spots and Cognitive Biases in AI Research and Alignment

The challenge of AI alignment is not just a technological issue but fundamentally an epistemic one. AI safety research predominantly relies on empirical validation, often detecting failures only after they manifest. However, certain risks—such as deceptive alignment and goal misspecification—may not be empirically testable until it is too late, necessitating a shift toward leading-indicator logical reasoning. This paper explores how mainstream AI research systematically filters out deep epistemic insight, hindering progress in AI safety. We assess the rarity of such insights, conduct an experiment testing large language models for epistemic blind spots, and propose structural reforms, including contrarian epistemic screening, decentralized collective intelligence mechanisms, and epistemic challenge platforms. Our findings suggest that while AGI may emerge through incremental engineering, ensuring its safe alignment likely requires an epistemic paradigm shift.