Introduction

In recent years, the Indian music industry has undergone a dramatic transformation due to the rapid rise of digital streaming services such as Gaana, JioSaavn, Wynk, and Spotify India. These platforms have revolutionized music consumption by making it more accessible and personalized than ever before. At the heart of this revolution lies artificial intelligence (AI), which curates playlists tailored to individual user preferences by analyzing extensive behavioral data such as listening history, song skips, replay rates, and time-of-day habits. While AI enhances user experience by enabling hyper-personalized content discovery, it simultaneously introduces complex legal and ethical challenges concerning equitable royalty distribution and artist rights in the digital age.

As AI becomes increasingly integral to music streaming services, its influence is no longer limited to personalization. Algorithms now hold the power to determine commercial visibility, favoring certain tracks, genres, or artists based on metrics that may lack transparency or fairness. In doing so, AI intermediates the relationship between creator and consumer, introducing an opaque layer that can materially affect the livelihood of musicians. The implications of this are significant: creators whose content is consistently included in AI-generated playlists gain exposure and revenue, while those who are excluded face potential invisibility, regardless of artistic merit.

The Legal Landscape: Copyright Law and Its Limits

The Copyright Act, 1957, remains the central statute governing intellectual property rights in India. It defines the scope of copyright protection, the rights of authors and performers, and the framework for licensing and assignment. Section 14 confers upon copyright holders exclusive rights to reproduce, distribute, perform, and communicate their work to the public.¹ Sections 18 and 19 regulate the transfer of these rights, mandating that assignments specify the scope, duration, and royalty arrangements.² These provisions, however, were conceived in an era dominated by physical media and traditional broadcasting. They do not address the use of AI-driven recommendation systems or algorithmic gatekeeping now prevalent in music streaming.

The Act presumes human-mediated access and reporting of usage data. However, when algorithmic systems dictate exposure and consumption, key performance indicators that trigger royalty payments may no longer be traceable in the same linear way. The lack of alignment between AI-generated dissemination and statutory assumptions regarding communication to the public calls for an urgent interpretive update to the law.

Algorithmic Gatekeeping and Royalty Disparities

A growing body of evidence suggests that algorithmic curation systems exhibit a significant bias toward mainstream artists. For instance, studies have shown that Spotify’s recommendation engine demonstrates a form of “popularity bias,” consistently favoring well-known artists at the expense of lesser-known or independent musicians.³ This results in skewed streaming numbers that do not accurately reflect the diversity of content available or consumer intent.

Further complicating the situation is Spotify’s controversial “Perfect Fit Content” initiative, where the platform reportedly commissioned music from anonymous or lesser-known artists and included them in popular playlists.⁴ This strategy allowed Spotify to reduce royalty payouts, as the platform was not obligated to pay major label rates to these contributors. This practice sparked debates about platform transparency and the potential undermining of fair compensation models for genuine creators.

Academic studies have also pointed out that independent artists affiliated with non-major labels tend to face significant disadvantages in streaming ecosystems dominated by algorithmic curation.⁵ Songs released under independent labels often receive fewer playlist placements, fewer promotional opportunities, and less visibility overall, even when their streaming performance metrics are comparable to those of major-label artists. This suggests that recommendation systems may be inherently skewed in favor of commercially dominant catalogues, limiting the discoverability and economic viability of independent music. As a result, independent artists may be systematically excluded from meaningful participation in the digital music economy, raising concerns of algorithmic discrimination embedded in private platform governance.

A major issue arises when AI begins to dictate which songs are heard and which are ignored. Songs featured on prominent algorithmic playlists receive disproportionately more streams, translating to greater revenue.⁶ This selection process is controlled by opaque, proprietary algorithms, making it nearly impossible for artists to understand or challenge how and why their works are included or excluded. As these algorithms are not regulated under current law, platforms wield enormous influence over commercial outcomes with little to no accountability.

Moreover, Section 31D of the Copyright Act, which enables statutory licensing for traditional broadcasters, does not extend to digital platforms.⁷ This omission leaves a regulatory gap for one of the most dominant modes of modern music distribution. In the absence of statutory oversight, streaming platforms are not obligated to provide transparent metrics, leaving copyright societies like the Indian Performing Rights Society (IPRS) unable to ensure fair and accurate royalty allocation.

Another issue is the long-tail effect of AI recommendations. Algorithms are optimized for engagement, often privileging mainstream content that guarantees higher interaction.⁸ This means that niche genres or independent musicians may be systematically filtered out, exacerbating inequality within the music economy. The economic stratification that emerges is thus algorithmically enforced, raising questions about whether a system of private technological governance should determine public cultural participation.

Contractual Dynamics and the Role of Good Faith

Beyond statutory law, artist-platform relationships are primarily governed by contract. These agreements typically promise a share of streaming revenue but remain silent on algorithmic

AI Music Challenges: How Artists Are Defending Their Work

visibility. A track that is technically hosted but never promoted due to algorithmic choices may earn little or nothing. This creates a power imbalance where platforms benefit from information asymmetry, while artists lack leverage or knowledge to protect their earnings.

The principle of good faith under Section 37 of the Indian Contract Act, 1872⁹, and the doctrine of unconscionability become pertinent. A strong argument can be made that contracts which ignore the role of algorithmic visibility may exploit weaker parties and violate principles of equity, fairness, and transparency.

In addition to economic fairness, there are also implications for artistic recognition. Contracts that do not acknowledge algorithmic suppression or provide safeguards for visibility fail to uphold the creator’s legitimate expectation of exposure. A track’s merit becomes secondary to algorithmic favorability, further eroding the artist’s control over how their work is received by audiences.

Performer Rights in an AI-Dominated Marketplace

Performers are further impacted by algorithmic suppression. Under Section 38 of the Copyright Act, performers enjoy neighboring rights over their live or recorded performances, which are distinct from the rights of composers and producers.¹⁰ Section 39A grants them moral rights, including the right to claim credit and object to distortion.¹¹ When AI systems consistently exclude performances by artists from minority, regional, or less-commercial backgrounds, they undermine both the economic and moral rights of those performers. While such exclusions may not meet the technical threshold of copyright infringement, they effectively erode statutory protections by marginalizing certain creators through automated decision-making.

The challenge lies in demonstrating intent or bias when decisions are made by a non-human entity. Courts and copyright authorities are ill-equipped to audit AI behavior or evaluate harm resulting from omission. The problem becomes even more serious in cases where AI mimics human curation so effectively that it escapes scrutiny entirely.

Comparative Perspectives: Learning from International Reforms

International developments provide useful benchmarks. The European Union’s Directive (EU) 2019/790 on Copyright in the Digital Single Market introduces transparency obligations that require platforms to inform authors and performers of how their works are being exploited.¹² This includes clear reporting on revenues and usage. In the United States, the Music Modernization Act of 2018 created the Mechanical Licensing Collective (MLC), a centralized body that manages blanket licenses and ensures equitable royalty distribution through data-driven transparency.¹³

Canada and Australia are also exploring statutory mechanisms to ensure algorithmic fairness in music streaming. Canadian copyright boards have signaled the importance of metadata transparency and equitable treatment for lesser-known artists. Similarly, Australian parliamentary committees have debated reforms to ensure independent musicians have tools to understand and contest algorithmic discrimination.

The Indian Gap and Urgency for Reform

India, by contrast, lacks any legal requirement for platforms to explain or disclose the functioning of their AI systems. The Copyright Rules, 2013, do not impose transparency obligations on digital services, and Section 31D remains restricted to legacy broadcasting. In this context, neither artists nor collecting societies have meaningful avenues to contest algorithmic practices that may be discriminatory, exclusionary, or arbitrary.

While the Competition Commission of India (CCI) could theoretically address algorithmic bias under Section 4 of the Competition Act, 2002, such remedies are reactive and complex, requiring a high threshold for proof.¹⁴ What India needs is a proactive, preventive legal framework tailored specifically to the music streaming ecosystem.

Further, India lacks judicial precedents on AI-related royalty disputes. The absence of case law reinforces uncertainty, leaving copyright societies and artists without doctrinal clarity. Law reform bodies and academic institutions must work collaboratively to explore doctrinal adaptations and potential interpretive approaches to AI-mediated exploitation.

Pathways Forward: Legislative and Policy Proposals

To bridge this gap, a multi-pronged legal reform strategy is essential. Firstly, Section 31D should be amended to bring digital streaming platforms under the statutory licensing regime, making them subject to similar obligations as traditional broadcasters. This would empower the government to impose reporting requirements and monitor unfair practices.

Further, a new legal provision should mandate algorithmic transparency in music recommendation systems. While preserving the trade secrecy of algorithms, platforms should be required to publish generalized information about the factors influencing playlist curation—such as popularity metrics, user behavior, or linguistic diversity. This would enable rights holders and regulators to detect and challenge systemic biases.

Additionally, licensing contracts between platforms and artists should include standard clauses affirming the right to fair algorithmic exposure. These clauses should offer a mechanism for auditing algorithmic performance or appealing against exclusion from playlists.

Apart from the above, India should establish a dedicated regulatory body or expand the jurisdiction of an existing authority to oversee fairness in digital music markets. This body could enforce disclosures, oversee audits, and handle complaints related to algorithmic discrimination.

Finally, the Ministry of Electronics and Information Technology (MeitY) or the Ministry of Information and Broadcasting (MIB) should collaborate with stakeholders to develop a Code of Ethics for AI in cultural content curation. This code could set out principles for inclusion, diversity, fairness, and transparency, and be supported by a sector-specific grievance redressal forum to resolve disputes efficiently.

Conclusion

AI-curated playlists are no longer just a novelty in music discovery; they are powerful cultural filters and economic gatekeepers. In deciding what gets streamed, they also decide who gets paid. This silent transformation, driven by algorithms, remains largely unregulated in India, placing artists at the mercy of invisible systems and unaccountable intermediaries.

The time has come for Indian copyright law to evolve. Through legislative amendments, contract reforms, and regulatory oversight, the legal system must reassert its role as a

protector of creative labor. Copyright is not just about control over intellectual output—it is about the right to participate in cultural life on fair and equal terms. In an age where algorithms dictate access, the law must guarantee that human creators are not left behind.

¹ The Copyright Act, No. 14 of 1957, INDIA CODE (1957).

² Ibid.

3 Patricia Betina, SPOTIFY UNWRAPPED: WHEN AI MISSES THE BEAT OMNISEARCH (2024),

4 Alexis Patridis, MOOD MACHINE BY LIZ PELLY REVIEW – A SAVAGE INDICTMENT OF SPOTIFY THE GUARDIAN (2025),

⁵ Luis Aguiar, Joel Waldfogel & Sarah Waldfogel, Playlisting favorites: Measuring platform bias in the music industry, 78 INTERNATIONAL JOURNAL OF INDUSTRIAL ORGANIZATION (2021).

6 Admin, HERE COMES THE CHANGE: FIXING LICENSING FOR THE STREAMING GENERATION ” LAWFUL LEGAL LAWFUL LEGAL

(2025), https://lawfullegal.in/here-comes-the-change-fixing-licensing-for-the-streaming-generation/.

⁷ Supra Note 1.

8 Dave Andre, AI MUSIC CHALLENGES: HOW ARTISTS ARE DEFENDING THEIR WORK ALL ABOUT AI (2024), https://www.allaboutai.com/resources/ai-music-challenges/.

⁹ The Indian Contract Act, No. 9 of 1872, INDIA CODE (1872).

¹⁰ Supra Note 1.

¹¹ Ibid.

¹² Directive 2019/790, of the European Parliament and of the Council of 17 April 2019 on copyright and related rights in the Digital Single Market, 2019 O.J. (L 130) 92

¹³ Orrin G. Hatch–Bob Goodlatte Music Modernization Act, Pub. L. No. 115-264, 132 Stat. 3676 (2018).

¹⁴ The Competition Act, No. 12 of 2003, INDIA CODE (2003).

Authored by: Mr. Ashutosh Jha

Law Student at NALSAR, Hyderabad