Explainability of AI systems is critical for users to take informed actions. Understanding "who" opens the black-box of AI is just as important as opening it. We conduct a mixed-methods study of how two different groups--people with and without AI background--perceive different types of AI explanations. Quantitatively, we share user perceptions along five dimensions. Qualitatively, we describe how AI background can influence interpretations, elucidating the differences through lenses of appropriation and cognitive heuristics. We find that (1) both groups showed unwarranted faith in numbers for different reasons and (2) each group found value in different explanations beyond their intended design. Carrying critical implications for the field of XAI, our findings showcase how AI generated explanations can have negative consequences despite best intentions and how that could lead to harmful manipulation of trust. We propose design interventions to mitigate them.

翻译：AI系统的可解释性对于用户做出明智决策至关重要。理解“谁”打开了AI的黑箱，与打开黑箱本身同等重要。我们开展了一项混合方法研究，探讨两个不同群体——具有AI背景和不具有AI背景的人——如何感知不同类型的AI解释。在定量层面，我们从五个维度分享用户感知；在定性层面，我们描述了AI背景如何影响理解，通过挪用和认知启发式的视角阐释差异。研究发现：(1)两个群体均对数字表现出无理由的信任，但原因不同；(2)每个群体都能从超出原始设计意图的解释中发现价值。本研究对XAI领域具有重要启示，展示了AI生成的解释即使出于善意也可能产生负面后果，以及这种后果如何导致对信任的有害操纵。我们提出设计干预措施来缓解这些问题。