Explanations are often promoted as tools for transparency, but they can also foster blind trust; users may assume reasoning is correct whenever model 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 perturb reasoning chains generated by vision-language models (VLMs) with three error types (omissions, contradictions, hallucinations) and measure their effects on user trust and error detection through a study with 800 participants, alongside a parallel detection task with models. Our findings reveal two key effects: (1) users often equate trust with outcome agreement regardless of flaws in the model’s reasoning, and (2) confident tones suppress scrutiny while leaving agreement and self-reported trust largely unchanged. These results highlight how CoT explanations can simultaneously clarify and mislead in tandem, and point to user-side scrutiny, not just model-side faithfulness, as a critical dimension for calibrated trust in AI reasoning. All code will be released publicly upon acceptance.

Current LLMs are trained to refuse potentially harmful input queries regardless of whether users actually had harmful intents, causing a tradeoff between safety and user experience. Through a study of 480 participants evaluating 3,840 query-response pairs, we examine how different refusal strategies affect user perceptions across varying motivations. Our findings reveal that response strategy largely shapes user experience, while actual user motivation has negligible impact. Partial compliance—providing general information without actionable details—emerges as the optimal strategy, reducing negative user perceptions by over 50% to flat-out refusals. Complementing this, we analyze response patterns of 9 state-of-the-art LLMs and evaluate how 6 reward models score different refusal strategies, demonstrating that models rarely deploy partial compliance naturally and reward models currently undervalue it. This work demonstrates that effective guardrails require focusing on crafting thoughtful refusals rather than detecting intent, offering a path toward AI safety mechanisms that ensure both safety and sustained user engagement.