Paper Number
ICIS2025-2801
Paper Type
Short
Abstract
Generative Artificial Intelligence (GenAI) 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 negatively affects AI use. To understand the inconsistency, we conduct a mixed-methods research to examine why users have two contradictory attitudes towards AI, i.e., algorithm appreciation and algorithm aversion. Specifically, we propose a model that explains how willful ignorance and motivated blindness moderate the effects of responsible algorithm attributes on algorithm appreciation or algorithm aversion. We test our model using the quantitative method. Our qualitative study seeks to develop an in-depth understanding of how and why users form algorithm appreciation and algorithm aversion. Our research contributes to the user-AI interaction, responsible AI, algorithm appreciation, and algorithm aversion.
Recommended Citation
Zhu, Yaping; Ding, Wenwen; and Sarkar, Sandip Kumar, "Algorithm Appreciation or Aversion? The Role of Psychological Biases" (2025). ICIS 2025 Proceedings. 36.
https://aisel.aisnet.org/icis2025/hti/hti/36
Algorithm Appreciation or Aversion? The Role of Psychological Biases
Generative Artificial Intelligence (GenAI) 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 negatively affects AI use. To understand the inconsistency, we conduct a mixed-methods research to examine why users have two contradictory attitudes towards AI, i.e., algorithm appreciation and algorithm aversion. Specifically, we propose a model that explains how willful ignorance and motivated blindness moderate the effects of responsible algorithm attributes on algorithm appreciation or algorithm aversion. We test our model using the quantitative method. Our qualitative study seeks to develop an in-depth understanding of how and why users form algorithm appreciation and algorithm aversion. Our research contributes to the user-AI interaction, responsible AI, algorithm appreciation, and algorithm aversion.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.
Comments
15-Interaction