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Paper Type
ERF
Abstract
Generative Artificial Intelligence (AI) has elicited much attention across disciplines and industries. On the one hand, we see an unstoppable growth of users. On the other hand, generative AI is often criticized for generating biased content that violates users’ trust and negatively affects AI use. To understand the inconsistency, we conduct a mixed-methods research to examine why users forgive AI bias. We propose a model of self-AI connection to explain why users forgive AI. We argue that the human-like features of AI can form a self-AI connection. And the self-AI connection can foster trust and satisfaction toward AI. We test our research model using quantitative study. The qualitative study seeks to develop an in-depth understanding of how the self-AI connection forms and why that leads to users’ willingness to forgive AI. Our research contributes to user-AI interaction and trust theory.
Paper Number
2128
Recommended Citation
Sarkar, Sandip Kumar and Zhu, Yaping, "Why Users Forgive AI for Generating Biased Content? Self-AI Connection Perspective" (2025). AMCIS 2025 Proceedings. 12.
https://aisel.aisnet.org/amcis2025/sig_hci/sig_hci/12
Why Users Forgive AI for Generating Biased Content? Self-AI Connection Perspective
Generative Artificial Intelligence (AI) has elicited much attention across disciplines and industries. On the one hand, we see an unstoppable growth of users. On the other hand, generative AI is often criticized for generating biased content that violates users’ trust and negatively affects AI use. To understand the inconsistency, we conduct a mixed-methods research to examine why users forgive AI bias. We propose a model of self-AI connection to explain why users forgive AI. We argue that the human-like features of AI can form a self-AI connection. And the self-AI connection can foster trust and satisfaction toward AI. We test our research model using quantitative study. The qualitative study seeks to develop an in-depth understanding of how the self-AI connection forms and why that leads to users’ willingness to forgive AI. Our research contributes to user-AI interaction and trust theory.
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