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Dan Shipper argues that AI models like GPT-4 can replace human intuition in fields like psychology where scientific explanations are lacking, advocating for using AI to drive progress even without full understanding.
This paper investigates the trade-off between plausibility and faithfulness in cross-lingual explanations from LLMs, finding that English-pivot explanations achieve higher span agreement with human rationales but suffer reduced causal faithfulness compared to native-language explanations.
The article explores the concept of illusions of understanding in scientific practice, discussing how ambiguous language, incomplete causal accounts, and satisfying but incomplete explanations can lead scientists to overlook deeper understanding.