Removing AI’s sentiment manipulation of personalized news delivery

Chuhan Wu, Fangzhao Wu (), Tao Qi, Wei-Qiang Zhang, Xing Xie and Yongfeng Huang ()
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Chuhan Wu: Tsinghua University
Fangzhao Wu: Microsoft Research Asia
Tao Qi: Tsinghua University
Wei-Qiang Zhang: Tsinghua University
Xing Xie: Microsoft Research Asia
Yongfeng Huang: Tsinghua University

Palgrave Communications, 2022, vol. 9, issue 1, 1-9

Abstract: 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.

Date: 2022
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DOI: 10.1057/s41599-022-01473-1

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