Who Gets Heard? Rethinking Fairness in AI for Music Systems
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909894104317952 |
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| author | Mehta, Atharva Chauhan, Shivam Sharma, Megha Xia, Gus Ganguli, Kaustuv Kanti Chandran, Nishanth Talat, Zeerak Choudhury, Monojit |
| author_facet | Mehta, Atharva Chauhan, Shivam Sharma, Megha Xia, Gus Ganguli, Kaustuv Kanti Chandran, Nishanth Talat, Zeerak Choudhury, Monojit |
| contents | In recent years, the music research community has examined risks of AI models for music, with generative AI models in particular, raised concerns about copyright, deepfakes, and transparency. In our work, we raise concerns about cultural and genre biases in AI for music systems (music-AI systems) which affect stakeholders including creators, distributors, and listeners shaping representation in AI for music. These biases can misrepresent marginalized traditions, especially from the Global South, producing inauthentic outputs (e.g., distorted ragas) that reduces creators' trust on these systems. Such harms risk reinforcing biases, limiting creativity, and contributing to cultural erasure. To address this, we offer recommendations at dataset, model and interface level in music-AI systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05953 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Who Gets Heard? Rethinking Fairness in AI for Music Systems Mehta, Atharva Chauhan, Shivam Sharma, Megha Xia, Gus Ganguli, Kaustuv Kanti Chandran, Nishanth Talat, Zeerak Choudhury, Monojit Computers and Society Multimedia Sound Audio and Speech Processing In recent years, the music research community has examined risks of AI models for music, with generative AI models in particular, raised concerns about copyright, deepfakes, and transparency. In our work, we raise concerns about cultural and genre biases in AI for music systems (music-AI systems) which affect stakeholders including creators, distributors, and listeners shaping representation in AI for music. These biases can misrepresent marginalized traditions, especially from the Global South, producing inauthentic outputs (e.g., distorted ragas) that reduces creators' trust on these systems. Such harms risk reinforcing biases, limiting creativity, and contributing to cultural erasure. To address this, we offer recommendations at dataset, model and interface level in music-AI systems. |
| title | Who Gets Heard? Rethinking Fairness in AI for Music Systems |
| topic | Computers and Society Multimedia Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2511.05953 |