Do listeners devalue AI-generated pop music? Exploring negative biases in listeners' responses to AI-labelled vs human-labelled pop music

The advancement of Artificial Intelligence (AI) in creative domains has sparked discussions regarding how listeners perceive and engage with AI-generated music. This study investigated listeners' emotions, perceptions, and attitudes toward AI-generated versus human-composed pop music. The study hypothesized that listeners would rate music labelled as AI-generated lower in terms of liking, quality, positive emotions, sensorial, imaginal, and experiential responses, as well as need for re-experience and purchase intentions, compared to music labelled as human-composed. Participants listened to eight AI-generated pop songs, four labelled as AI-generated and four labelled as human-composed. They then rated each song on various dimensions. To ensure a balanced design, label assignment on composer identity was fully randomized across both participants and songs. Contrary to the hypotheses, the participants rated pop songs labelled as AI-generated more highly in positive emotions, including happiness, interest, awe, and energy, compared to those labelled as human-composed. No significant differences were found between purported composer identity in the remaining dimensions. These results suggest that while the perception of AI authorship does influence listeners, the effects are primarily affective rather than sensorial, imaginal, experiential, or behavioural. Notably, considering that listeners rated pop songs labelled as AI-generated more positively in emotions, the findings imply that AI-generated music may be more readily accepted than previously assumed.

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Publication Details

Journal
Computers in Human Behavior Artificial Humans
Published
2025-10-09
DOI
https://doi.org/10.1016/j.chbah.2025.100217
Primary Topic
Explainable Artificial Intelligence (XAI)
Type
article
Field-Weighted Citation Impact
0.00
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article

Do listeners devalue AI-generated pop music? Exploring negative biases in listeners' responses to AI-labelled vs human-labelled pop music

Andree Hartanto, Eddie M. W. Tong, Suqi Chia
Computers in Human Behavior Artificial Humans
Explainable Artificial Intelligence (XAI)
article

Do listeners devalue AI-generated pop music? Exploring negative biases in listeners' responses to AI-labelled vs human-labelled pop music

Andree Hartanto, Eddie M. W. Tong, Suqi Chia
article en

Abstract

The advancement of Artificial Intelligence (AI) in creative domains has sparked discussions regarding how listeners perceive and engage with AI-generated music. This study investigated listeners' emotions, perceptions, and attitudes toward AI-generated versus human-composed pop music. The study hypothesized that listeners would rate music labelled as AI-generated lower in terms of liking, quality, positive emotions, sensorial, imaginal, and experiential responses, as well as need for re-experience and purchase intentions, compared to music labelled as human-composed. Participants listened to eight AI-generated pop songs, four labelled as AI-generated and four labelled as human-composed. They then rated each song on various dimensions. To ensure a balanced design, label assignment on composer identity was fully randomized across both participants and songs. Contrary to the hypotheses, the participants rated pop songs labelled as AI-generated more highly in positive emotions, including happiness, interest, awe, and energy, compared to those labelled as human-composed. No significant differences were found between purported composer identity in the remaining dimensions. These results suggest that while the perception of AI authorship does influence listeners, the effects are primarily affective rather than sensorial, imaginal, experiential, or behavioural. Notably, considering that listeners rated pop songs labelled as AI-generated more positively in emotions, the findings imply that AI-generated music may be more readily accepted than previously assumed.

Computers in Human Behavior Artificial HumansVol. 6
National University of Singapore (SG), Singapore Management University (SG)
Openalex Percentile: Top 85%
Explainable Artificial Intelligence (XAI)
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