本研究以社會歸因理論、信任修復理論及權力理論為基礎，探討當人機混合團隊決策發生錯誤時，不同權力地位的 AI 以不同歸因策略道歉，使用者對AI 的信任評價、信任修復效果及責任歸屬之影響。
研究採用線上實驗法，為 2 × 2 受試者間實驗設計，以 AI 權力與歸因策略（內在歸因、外在歸因）為兩項自變項，建構人機協作決策情境。研究結果顯示，首先，在 AI 發生錯誤後，高權力 AI 相較於低權力 AI 被歸屬更多責任，而人類則承擔較少責任，顯示使用者會依據 AI 的決策權力重新分配責任。其次，道歉後，相較於採取外在歸因，採取內在歸因的 AI 能使使用者維持較高的信任；然而，道歉本身仍不足以使信任恢復至犯錯前的初始水準。第三，AI 權力與歸因方式對 AI 責任歸屬具有交互作用。在高權力 AI 情境下，採取內在歸因時，AI 被歸屬的責任高於採取外在歸因；相反地，在低權力 AI 情境下，採取外在歸因時，AI 被歸屬的責任高於採取內在歸因。此外，各實驗條件皆呈現 AI 道歉後 AI 責任增加、人類責任下降的趨勢，顯示使用者會在道歉後重新調整 AI 與人類之間的責任分配。
整體而言，本研究補充了組織中人機協作決策情境中有關信任修復、信任評估與責任歸屬的相關討論，也進一步呈現參與者在人機互動不同階段中的信任與責任變化歷程。

This study draws on attribution theory, trust repair theory, and power theory to examine how different apology attribution strategies adopted by AI with different levels of power influence users' trust evaluation, trust repair, and responsibility attribution following decision-making errors in Human–AI Mixed Teams.
An online 2 × 2 between-subjects experiment was conducted, with AI power (high vs. low) and attribution strategy (internal vs. external attribution) as the independent variables in a Human–AI Mixed Team decision-making scenario. The results indicate that, first, after an AI error occurred, high-power AI was assigned significantly more responsibility than low-power AI, whereas human team members were assigned less responsibility, suggesting that users redistribute responsibility according to the AI's decision-making authority. Second, compared with external attribution, AI adopting an internal attribution strategy maintained higher levels of user trust following the apology. However, the apology itself was insufficient to restore trust to its pre-error level. Third, AI power and attribution strategy had a significant interaction effect on AI responsibility attribution. Under the high-power AI condition, internal attribution resulted in greater AI responsibility than external attribution, whereas the opposite pattern was observed under the low-power AI condition. Furthermore, across all experimental conditions, participants attributed greater responsibility to AI and less responsibility to human teammates after the apology, indicating that users dynamically adjusted the distribution of responsibility between AI and humans.
Overall, this study contributes to the literature on trust repair, trust evaluation, and responsibility attribution in Human–AI Mixed Teams and provides a deeper understanding of how trust and responsibility attribution evolve across different stages of Human–AI interaction.

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