AI Composer Bias: Listeners Like Music Less When They Think It Was Composed by an AI
The use of artificial intelligence (AI) to compose music is becoming mainstream. Yet, there is a concern that listeners may have biases against AIs. Here, we test the hypothesis that listeners will like music less if they think it was composed by an AI. In Study 1, participants listened to excerpts...
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Veröffentlicht in: | Journal of experimental psychology. Applied 2023-09, Vol.29 (3), p.676-692 |
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Zusammenfassung: | The use of artificial intelligence (AI) to compose music is becoming mainstream. Yet, there is a concern that listeners may have biases against AIs. Here, we test the hypothesis that listeners will like music less if they think it was composed by an AI. In Study 1, participants listened to excerpts of electronic and classical music and rated how much they liked the excerpts and whether they thought they were composed by an AI or human. Participants were more likely to attribute an AI composer to electronic music and liked music less that they thought was composed by an AI. In Study 2, we directly manipulated composer identity by telling participants that the music they heard (electronic music) was composed by an AI or by a human, yet we found no effect of composer identity on liking. We hypothesized that this was due to the "AI-sounding" nature of electronic music. Therefore, in Study 3, we used a set of "human-sounding" classical music excerpts. Here, participants liked the music less when it was purportedly composed by an AI. We conclude with implications of the AI composer bias for understanding perception of AIs in arts and aesthetic processing theories more broadly.
Public Significance Statement
Artificial intelligence (AI)-computers making intelligent decisions or emulating humans-is revolutionizing the music industry. Yet, very little is known about how people emotionally respond to AI-generated music. The findings of the present work indicate that listeners tend to be biased against music that they think was created by an AI if the music itself does not fit expectations of what an AI could create. |
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ISSN: | 1076-898X 1939-2192 |
DOI: | 10.1037/xap0000447 |