Daniel B Shank, Courtney Stefanik, Cassidy Stuhlsatz, Kaelyn Kacirek, Amy M Belfi
{"title":"人工智能作曲家偏见:当听众认为音乐是由人工智能创作时,他们就不那么喜欢音乐了。","authors":"Daniel B Shank, Courtney Stefanik, Cassidy Stuhlsatz, Kaelyn Kacirek, Amy M Belfi","doi":"10.1037/xap0000447","DOIUrl":null,"url":null,"abstract":"<p><p>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. (PsycInfo Database Record (c) 2023 APA, all rights reserved).</p>","PeriodicalId":48003,"journal":{"name":"Journal of Experimental Psychology-Applied","volume":"29 3","pages":"676-692"},"PeriodicalIF":2.7000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"AI composer bias: Listeners like music less when they think it was composed by an AI.\",\"authors\":\"Daniel B Shank, Courtney Stefanik, Cassidy Stuhlsatz, Kaelyn Kacirek, Amy M Belfi\",\"doi\":\"10.1037/xap0000447\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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. (PsycInfo Database Record (c) 2023 APA, all rights reserved).</p>\",\"PeriodicalId\":48003,\"journal\":{\"name\":\"Journal of Experimental Psychology-Applied\",\"volume\":\"29 3\",\"pages\":\"676-692\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Experimental Psychology-Applied\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1037/xap0000447\",\"RegionNum\":3,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PSYCHOLOGY, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Experimental Psychology-Applied","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1037/xap0000447","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PSYCHOLOGY, APPLIED","Score":null,"Total":0}
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 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. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
期刊介绍:
The mission of the Journal of Experimental Psychology: Applied® is to publish original empirical investigations in experimental psychology that bridge practically oriented problems and psychological theory. The journal also publishes research aimed at developing and testing of models of cognitive processing or behavior in applied situations, including laboratory and field settings. Occasionally, review articles are considered for publication if they contribute significantly to important topics within applied experimental psychology. Areas of interest include applications of perception, attention, memory, decision making, reasoning, information processing, problem solving, learning, and skill acquisition.