aiqbench analysis
This research investigates how Chain-of-Thought (CoT) explanations in vision language models can inadvertently foster confirmation bias, causing users to trust flawed reasoning simply because the output aligns with their expectations. The study demonstrates that a confident delivery tone can further suppress error detection, highlighting a critical safety challenge where transparency mechanisms may simultaneously mislead rather than clarify. These findings underscore the urgent need for NLP systems to design explanations that actively encourage scrutiny and critical thinking instead of fostering blind reliance.
Critical or Compliant? The Double-Edged Sword of Reasoning in Chain-of-Thought Explanations
arXiv:2511.12001v3 Announce Type: replace Abstract: Explanations are often promoted as tools for transparency, but they can also foster confirmation bias; users may assume reasoning is correct whenever outputs appear acceptable. We study this double-edged role of Chain-of-Thought (CoT) explanations in multimodal moral scenarios by systematically perturbing reasoning chains and manipulating delivery tones. Specifically, we analyze reasoning errors in vision language models (VLMs) and how they impact user trust and the ability to detect errors. Our findings reveal two key effects: (1) users often equate trust with outcome agreement, sustaining reliance even when reasoning is flawed, and (2) the confident tone suppresses error detection while maintaining reliance, showing that delivery styles can override correctness. These results highlight how CoT explanations can simultaneously clarify and mislead, underscoring the need for NLP systems to provide explanations that encourage scrutiny and critical thinking rather than blind trust. All code will be released publicly.
Title:Critical or Compliant? The Double-Edged Sword of Reasoning in Chain-of-Thought Explanations
Abstract:Explanations are often promoted as tools for transparency, but they can also foster confirmation bias; users may assume reasoning is correct whenever outputs appear acceptable. We study this double-edged role of Chain-of-Thought (CoT) explanations in multimodal moral scenarios by systematically perturbing reasoning chains and manipulating delivery tones. Specifically, we analyze reasoning errors in vision language models (VLMs) and how they impact user trust and the ability to detect errors. Our findings reveal two key effects: (1) users often equate trust with outcome agreement, sustaining reliance even when reasoning is flawed, and (2) the confident tone suppresses error detection while maintaining reliance, showing that delivery styles can override correctness. These results highlight how CoT explanations can simultaneously clarify and mislead, underscoring the need for NLP systems to provide explanations that encourage scrutiny and critical thinking rather than blind trust. All code will be released publicly.
Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
arXiv:2511.12001 [cs.CL]
(or arXiv:2511.12001v3 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2511.12001 Focus to learn more arXiv-issued DOI via DataCite
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