Chain-of-thought is not explainability: Our Takeaways

The paper "Chain-of-thought is not explainability" critiques the assumption that Chain-of-Thought (CoT) prompting enhances both performance and transparency in language models, arguing instead that CoT often serves as a post-hoc rationalization that obscures the true decision-making process. Presented at a recent conference, the study highlights experiments demonstrating that CoT outputs, while coherent, frequently fail to reflect the actual reasoning of models, as seen in phenomena like the "Answer is Always A" bias. This misalignment underscores the risks of relying solely on CoT for auditing, due to the "Illusion of Explanatory Depth," which can lead to overlooking systemic issues such as biases and overfitting. The paper suggests alternative methods like counterfactual testing and self-consistency checks to ensure AI model reliability, emphasizing the need for rigorous scrutiny beyond surface-level explanations. It advocates treating CoT as a testable output rather than a definitive explanation, recommending the establishment of lightweight "rationale audits" to move from mere storytelling to genuine accountability in AI systems.