Removing AI's sentiment manipulation of personalized news delivery

DOI number:

10.1057/s41599-022-01473-1

Journal:

Humanities & Social Sciences Communications

Abstract:

Artificial intelligence (AI) is empowering personalized online news delivery to accommodate people’s information needs and combat information overload. However, AI models learned from user data are inheriting and amplifying some underlying human prejudice such as the sentiment bias of news reading, which may lead to potential negative societal effects and ethical concerns. Here, substantial evidence shows that AI is manipulating the sentiment orientation of news displayed to users by promoting the presence chance of negative news, even if there is no human interference. To mitigate this manipulation, a sentiment-debiasing method based on a decomposed adversarial learning framework is proposed, which can reduce 97.3% of sentiment bias with only 2.9% accuracy sacrifice. Our work provides the potential in improving AI’s responsibility in many human-centered applications such as online journalism and information spread.

Co-author:

Tao Qi,Wei-Qiang Zhang,Xing Xie

First Author:

Chuhan Wu

Indexed by:

Journal paper

Correspondence Author:

Fangzhao Wu,Yongfeng Huang

Translation or Not:

no

Date of Publication:

2022/12/20