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Removing AI’s sentiment manipulation of personalized news delivery

Author

Listed:
  • 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
    Zhongguancun Laboratory
    Tsinghua University)

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.

Suggested Citation

  • Chuhan Wu & Fangzhao Wu & Tao Qi & Wei-Qiang Zhang & Xing Xie & Yongfeng Huang, 2022. "Removing AI’s sentiment manipulation of personalized news delivery," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-9, December.
    • Handle: RePEc:pal:palcom:v:9:y:2022:i:1:d:10.1057_s41599-022-01473-1
    DOI: 10.1057/s41599-022-01473-1
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    References listed on IDEAS

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