ABSTRACT
Algorithm knowledge of users plays a crucial role in avoiding them from algorithm bias in recommendation systems. Gender of users has been found to correlate with algorithm bias, but also leaving behind a question of whether this relationship can be described by algorithm knowledge. By using Weibo as an example system, we clarify the aforementioned question from a digital divide theory perspective. We combine a traditional method (questionnaire) with a deep learning computational method to explain algorithm bias in two sequential studies. Our findings suggest that algorithm knowledge solely works for men while fails to protect women. Who users follow helps determine what information they are exposed to on Weibo, and this renders female users’ algorithm knowledge useless. This work provides a valuable perspective on algorithm bias: we view algorithm bias as a new digital divide and contribute to the understanding of gender differences by applying the digital divide perspective. Methodologically, we contribute by integrating traditional and computational methods to explain algorithm bias from a folk theory perspective.
Acknowledgments
The authors gratefully acknowledge the support from Dr Tong Ji, and Dr Di He.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1 Cyberspace Administration of China (2022) 解读《互联网信息服务算法推荐管理规定》, Available at: http://www.gov.cn/zhengce/2022–01/04/content_5666428.htm (accessed 10 January 2022)
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Yang Zhang
Yang Zhang is a lecturer at the School of New Media and Communication at Tianjin University. She received her Ph.D. from School of New Media and was an electronic engineering postgraduate student at Peking University. Her research interests concern computational social science, algorithm, AI ethics, and cross-cultural study.
Huashan Chen
Huashan Chen is a research fellow in sociology area at Chinese Academy of Social Sciences. His research interests lie in computational social science, digital divide and social development.