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Bias in AI Algorithms and Its Impact on Musical Diversity
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Author(s): Saroj Sandeep Phalke (Vishwakarma Institute of Information Technology, India), Gitanjali Shinde (Vishwakarma Institute of Technology, Pune, India), Grishma Bobhate (Vishwakarma Institute of Technology, Pune, India), Poonam Railkar (Smt Kashibai Navale College of Engineering, Vadgaon, India), Haribhau R. Bhapkar (Central University of Kashmir Green Campus, Ganderbal, India)and Sonal Fatangare (Vishwakarma Institute of Technology, Pune, India)
Copyright: 2026
Pages: 28
Source title:
Artificial Intelligence in Music Production: Innovations, Practices, and Industry Implications
Source Author(s)/Editor(s): Randy Joy Magno Ventayen (Pangasinan State University, Phillippines)
DOI: 10.4018/979-8-3373-6279-3.ch004
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Abstract
Artificial intelligence (AI) is transforming music creation, distribution, and discovery, offering new opportunities but also reinforcing bias. When trained on datasets dominated by specific genres or demographics, AI systems often amplify mainstream trends while marginalizing niche artists and underrepresented communities. This chapter explores how such bias emerges through skewed data and algorithmic design, and how it narrows musical diversity by favoring “safe” and commercially viable outputs. Case studies, including the EU's Fair MusE project, reveal the impact on fairness and cultural representation. Responses such as diversity-aware algorithms, enriched datasets, and transparent systems are examined alongside ethical and policy concerns. The chapter concludes that sustaining musical diversity in the AI era demands accountability, innovation, and collaboration between technologists, artists, and regulators to ensure technology amplifies rather than diminishes global musical expression.
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