使用算法和不太受欢迎的哥伦比亚音乐进行个性化和分析:音乐中的目标导向机制,情绪识别
Juan Sebastián Gómez-Cañón1,2, Thomas Magnus Lennie3,4, Tuomas Eerola3
1Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain.
概括
个性化音乐情感识别 (MER) 系统有风险敏感的分析,特别是政治音乐. 政治身份影响了情绪反应,人工智能模型可以检测到这些情绪反应,这引发了对滥用的伦理担忧.
科学领域:
- 人工智能的人工智能
- 心理学 心理学 心理学
- 社会学 社会学 社会学
背景情况:
- 个性化音乐情绪识别 (MER) 系统越来越多地用于福祉.
- 充满政治情绪的音乐可以引起强烈的情绪反应.
- 哥伦比亚传统音乐为研究政治影响的情绪提供了一个独特的背景.
研究的目的:
- 通过个性化的MER系统,调查敏感分析的潜力.
- 分析政治身份如何塑造对政治音乐的情感反应.
- 评估AI驱动的情绪分析在敏感环境中的伦理影响.
主要方法:
- 在49名参与者的数据上训练有素的个性化机器学习模型.
- 专注于哥伦比亚的瓦伦纳托斯和科里多斯,具有明确的政治内容.
- 预测诱发了情绪反应,特别是负面情绪.
主要成果:
- 政治认同显著影响与政治音乐的情感体验.
- 在某种程度上,MER模型捕获了情绪反应的变化.
- 个性化的MER系统可以通过音乐诱导的情绪来潜在地识别用户的政治倾向.
结论:
- 对音乐的主观情绪反应可能构成敏感的个人数据.
- 在没有保障的情况下部署人工智能驱动的情绪分析会带来重大的伦理风险.
- 在情感分析中未能考虑社会政治背景,可能会放大伤害和排斥,特别是对弱势群体.
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