Ethical Frameworks for Responsible Music AI: Balancing Creativity, Ownership, and Cultural Impact

All rights remain with the authors under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Proc. of the 17th Int. Symposium on Computer Music Multidisciplinary Research, London, United Kingdom, 2025 Ethical Frameworks for Responsible Music AI: Balancing Creativity, Ownership, and Cultural Impact Sridharan Sankaran [0009-0005-1341-6384] Fynro Advisory Services, Chennai, India [email protected] Abstract. The rapid rise of music AI systems—such as OpenAI’s Jukebox, Suno AI, and virtual ensembles like Mave—has transformed creative workflows, with 60% of musicians adopting these tools. They lower barriers and enable rapid composition but also introduce ethical challenges that threaten cultural "place" (local musical identities) and creative "space" (authorship, agency, equity). AI models trained on Eurocentric datasets (80–85% Western genres) risk appropriating non-Western forms like raga, maqam, or gagaku. Further concerns include copyright disputes, algorithmic bias toward 4/4 rhythms, opaque architectures, diminished artist agency, environmental costs from large-scale models, and deepfake misuse mimicking artists such as Drake and The Weeknd. Economic inequity remains stark, with only 0.4% of musicians earning sustainable streaming income, while human-AI performances pose safety risks. This position paper proposes a comprehensive ethical framework for responsible music AI. It draws on Confucian harmony to foster collective creativity, Buddhist compassion to minimize harm, and Shinto animism to promote respectful AI collaboration. These align with Human-Centered AI (HCAI) principles, techno-moral virtues like empathy and honesty, and the UN Sustainable Development Goals. We advocate for block-chain-based attribution to clarify ownership, community-governed datasets with at least 45% non-Western content to ensure authenticity, culturally sensitive design to protect sacred music, transparent metadata to counter deepfakes, bias audits to promote diversity, and energy-efficient models for sustainability. Case studies—including Tone Transfer’s exclusion of guqin and Suno AI’s legal challenges—underscore the need for frameworks beyond UNESCO’s generic guidelines to foster ethically grounded, culturally resonant music ecosystems while addressing emerging safety risks from human-AI performances. Keywords: Music AI, Cultural Ethics, Non-Western Music. 1 Introduction Artificial intelligence (AI) is reshaping music creation, production, and distribution through tools like OpenAI’s Jukebox, Google’s Magenta, MusicLM, Suno AI, and AIVA, enabling rapid composition, mixing, and voice synthesis, with around 60% of Proc. of the 17th International Symposium on CMMR, London, UK, Nov. 3-7, 2025 698 S. Sankaran musicians already adopting these technologies to enhance their creative processes [1, 2, 29]. These systems democratize music by lowering creative barriers, allowing users with minimal expertise to produce polished works and opening new artistic possibilities. Yet they also pose ethical challenges that disrupt cultural “place” and creative “space”, central to the CMMR 2025 theme. Key risks include copyright infringement, cultural appropriation, algorithmic bias, opacity, reduced creator agency, environmental harm, deepfake misuse, and widening inequities [2, 3, 4, 5, 6, 22]. The dominance of Eurocentric datasets (80–85% Western content) marginalizes traditions like gagaku, raga, or guqin, threatening cultural identities [6, 7, 8]. AI-driven efficiencies further extract value from musical commons without fair compensation, leaving only 0.4% of artists with sustainable streaming income [5, 9, 24]. Such risks endanger place-based identities (e.g., East Asian, Global South gen-res) and creative spaces (e.g., authorship, labor fairness) [6, 7]. Legal uncertain-ties—including non-human authorship and unauthorized training data—fuel disputes like RIAA’s case against Suno AI [2, 10], while deepfakes heighten concerns over consent and authenticity [2, 4]. Environmental costs and safety issues in human-AI co-performances, such as injuries from robotic instruments, further complicate matters [4, 11]. Current frameworks like UNESCO’s AI Ethics Guide-lines and OECD AI Principles lack music-specific safeguards [11, 12, 21]. This position paper advances a new ethical framework for responsible music AI, integrating East Asian philosophies, Human-Centered AI (HCAI), technomoral virtues, and the UN SDGs [4, 5, 6, 13, 25]. Drawing on Huang et al.’s ‘agonistic interdisciplinarity,’ it calls for inclusive collaboration with ethnomusicology and cultural studies [25]. A refined risk typology and roadmap propose solutions such as blockchain attribution, community datasets, culturally sensitive design, and transparency standards to protect heritage and equitable creative labor [2, 6, 14]. 2 Background 2.1 Music AI Overview Music AI has transformed creative workflows, streamlining composition, pro-duction, and distribution [1, 2]. Beginning with the 1957 Illiac Suite, it advanced through machine learning and datasets like the Lakh MIDI Dataset [1, 7]. Today’s tools— OpenAI’s Jukebox, Google’s Magenta and MusicLM, Suno AI, AIVA, and iZotope’s Ozone—enable genre classification, melody generation, mixing, mastering, and voice synthesis [1, 2, 14]. Spotify applies collaborative filtering for personalized recommendations [1]. These systems democratize creation by lowering costs and helping users without expertise [1, 5]. Suno AI generates text-based songs, AIVA composes for media, Supertone clones K-pop voices, and Jukedeck drives AI music in Asia [2, 6]. Yet accessibility risks cultural homogenization, labor displacement, and ethical misuse, threatening the cultural “place” and creative “space” central to this theme [5, 6, 7]. Proc. of the 17th International Symposium on CMMR, London, UK, Nov. 3-7, 2025 699 Ethical Frameworks for Responsible Music AI 2.2 Key Ethical Issues Music AI’s ethical challenges threaten identities and creator agency, requiring urgent attention: − Ownership and Copyright: AI music raises legal ambiguities. U.S. law excludes non-human authorship, and training on copyrighted data without consent risks infringement, as in RIAA lawsuits against Suno AI [2, 3, 10]. Disputes over whether rights belong to developer, user, or machine complicate licensing [1, 2]. − Cultural Misrepresentation: Datasets like the Million Song Dataset (80–85% Western genres) underrepresent traditions (e.g., gagaku, raga, pansori), fueling erasure and misrepresentation [6, 7, 8]. This skew alienates audiences and distorts cultural “place” [6, 14]. − Creator Agency: AI works, like Taryn Southern’s AI-composed album, can erode artistic control and depth, reducing music to impersonal outputs [1, 4, 14]. Musicians value AI as collaborator, not substitute, emphasizing human intent [14]. − Labor and Value Gap: AI efficiencies widen inequities, with only 0.4% of artists earning sustainable streaming income [5, 9]. Job loss and labor devaluation threaten creative “space” [5, 15]. − Deepfakes: AI-generated vocals mimicking artists (e.g., Suno’s “Prancing Queen”) raise consent, authenticity, and market dilution issues, flooding eco-systems with imitations [2, 4, 6]. These issues highlight the need for music-specific ethical frameworks. General AI guidelines (e.g., UNESCO, OECD) lack tailored solutions for music’s cultural and creative nuances [11, 12, 21]. East Asian philosophies, like Confucian harmony, and HCAI principles offer pathways to address these challenges, preserving musical heritage and equity [6, 13, 16]. 3 Ethical Challenges in Music AI Music AI’s transformative potential is tempered by ethical risks that disrupt cultural “place” (local identities) and creative “space” (authorship, agency, equity), central to our theme “Sound, Music: Space, Place.” We propose a risk typology—ownership, appropriation, bias, transparency, creator agency, environ-mental impact, inequity, misuse, and safety—synthesizing legal, cultural, and technical perspectives to structure responses and guide our framework. 3.1 Ownership Ownership disputes dominate music AI ethics as AI-generated works unsettle copyright. U.S. law excludes non-human authorship, leaving ambiguity over developer, user, or AI rights [2, 3, 10]. Training on copyrighted music without con-sent, as in RIAA’s Suno lawsuits, risks infringement, while collective dataset use challenges individual copyright regimes [2, 10, 22, 24]. Suno outputs may embed protected melodies or lyrics, complicating licensing and royalties [2, 22]. Majumdar calls for new Proc. of the 17th International Symposium on CMMR, London, UK, Nov. 3-7, 2025 700 S. Sankaran categories like joint authorship to recognize human–AI collaboration [3]. Fox et al. warn that copyrighted dataset use dilutes royalties, deepening uncertainty [22]. Without clear models, creators face financial and legal instability, undermining creative “space” [1, 5]. 3.2 Cultural Appropriation Music AI often misrepresents traditions, eroding cultural “place.” Datasets like the Million Song and Lakh MIDI (80–85% Western) underrepresent non-Western instruments (veena, erhu) and scales (raga, maqam), yielding inauthentic outputs, e.g., Irish jigs lacking ornamentation [6, 7, 8, 20, 25]. Huang et al. warn of “data colonialism,” exploiting cultures without supporting their ecosystems, echoed in Irish concerns over AIgenerated tunes [25]. Rohrmeier stresses that models fail to capture cultural context, worsening appropriation [23]. Huang and Kanhov note misuse of sacred instruments like the guqin or nadaswaram can offend communities [6, 16, 20]. Wang et al. show only 15–20% non-Western representation, risking erasure [7]. Such skew alienates audiences and commodifies heritage [5, 14, 20, 23]. 3.3 Bias Algorithmic bias in music AI stems from Eurocentric datasets, skewing outputs toward Western aesthetics [7, 14, 25]. Newman et al. report musicians’ concerns about training data reflecting “Eurocentrism,” limiting diversity in genres and styles [14]. For instance, AI-generated music often favors 4/4 rhythms over com-plex polyrhythms common in African or Indian music [7]. This bias reinforces a “superstar economy,” reducing visibility for marginalized artists and homogenizing musical “place” [1, 15]. Addressing bias requires diverse, representative datasets, co-curated with cultural stakeholders, as advocated by FAIR Data Principles and Huang et al.’s call for agonistic interdisciplinarity [17, 25]. 3.4 Transparency Lack of transparency in music AI processes undermines trust and accountability. Oğul emphasizes that proprietary algorithms and undisclosed training data obscure how outputs are generated, hindering informed consent [11]. For example, Suno’s failure to disclose copyrighted training data has fueled legal disputes [2]. Newman et al. note creators’ demand for explainable AI to understand and control outputs [14]. Without transparency, audiences cannot distinguish human-made from AI-generated works, eroding authenticity in creative spaces [4, 11]. 3.5 Creator Agency AI may diminish creator agency by producing works lacking intent or depth, yet many musicians view it as a collaborator. Newman et al. found artists welcome AI’s support but resist its dominance, fearing loss of identity [14]. Canyakan notes AI works, like Taryn Southern’s album, feel impersonal [1, 4]. Rohrmeier’s embodiment challenge Proc. of the 17th International Symposium on CMMR, London, UK, Nov. 3-7, 2025 701 Ethical Frameworks for Responsible Music AI stresses AI’s inability to capture bodily performance, as in live jazz, while his metacreativity challenge shows AI cannot innovate compositional forms (e.g., embedding a fugue in a sonata) [23]. Fox et al. argue AI music lacks emotional resonance, with MuseNet failing to capture personal experiences [22]. Flick and Worrall warn that overreliance blurs the “artistic essence” of creativity [4]. HCAI principles stress user control to preserve agency [13, 23]. 3.6 Environmental Impact The environmental footprint of music AI, particularly generative models, is significant but under addressed. Oğul criticizes the absence of sustainability in mu-sic AI guidelines, noting high energy consumption in training large models [11]. The quest for model quality often comes at a high computational cost, leading to vast energy consumption and greenhouse gas emissions. Douwes et al. demonstrated [27] that lighter model architectures can produce high-quality samples while maintaining more sustainable energy consumption than bigger models, highlighting the need to put computational costs at the center of deep learning research priorities. Douwes et al. also investigated [26] the energy implications of neural audio synthesis models, noting that the absence of energy consumption criteria falls within the broader problem of lacking evaluation methods. For in-stance, cloud-based systems like Jukebox require substantial computational re-sources, contributing to carbon emissions [5]. Daoist ethics, as Huang et al. suggest, emphasize frugality to mitigate environmental harm [16]. This risk challenges sustainable music ecosystems, impacting the broader “place” of global creativity [5, 11]. 3.7 Economic Inequity Music AI exacerbates economic inequities, widening the value gap. Clancy re-ports that only 0.4% of artists earn sustainable streaming income, as AI tools re-duce production costs but concentrate profits among platforms, diverting value from musical communities [5, 24]. It's important to distinguish the existing im-pact of streaming platforms on music consumption and revenues from the specific impact of AI-generated music distribution on these same platforms. Job dis-placement in roles like mixing or composing threatens livelihoods, as seen with automated tools like Ozone. Gera notes that corporate AI adoption often devalues human labor, marginalizing grassroots creators [15]. Equitable frameworks must address this imbalance to protect creative "space" [2, 5]. 3.8 Misuse and Deepfakes Misuse, including deepfakes, poses cultural and ethical risks. Suno’s AI-generated vocals mimicking artists (e.g., ABBA-like “Prancing Queen”) raise consent and authenticity concerns, flooding markets with imitations [2, 4, 6]. Huang et al. warn that deepfakes of sacred music disrupt cultural integrity [6, 16]. Flick and Worrall highlight malicious misuse, such as AI-generated propaganda, necessitating safeguards [4]. These risks distort musical “place” by commodifying identities and undermining trust [2, 14]. Proc. of the 17th International Symposium on CMMR, London, UK, Nov. 3-7, 2025 702 S. Sankaran 3.9 Safety Physical safety risks arise in human-AI music collaborations, particularly in performance settings. Flick and Worrall cite robotic art installations where faulty AI could cause collisions or injuries [4]. For example, AI-driven percussion robots in live shows require rigorous safety protocols [4]. These risks, though less dis-cussed, are critical for ensuring safe creative “space” in AI-augmented performances [5, 6]. This typology underscores the need for music-specific ethical frameworks. General AI guidelines (UNESCO, OECD) lack granularity for music’s cultural and creative nuances [11, 12, 21]. Our framework, integrating East Asian philosophies, HCAI principles, and technomoral virtues, aims to mitigate these risks, fostering equitable and culturally respectful music AI ecosystems [6, 13, 16]. 4 Proposed Framework To address the ethical challenges of music AI outlined in Section 3—ownership, cultural appropriation, bias, transparency, creator agency, environmental impact, economic inequity, misuse, and safety—we propose a novel ethical framework. This framework integrates East Asian philosophies (e.g., Confucian harmony, Buddhist ethics), HCAI principles, technomoral virtues, and UN Sustainable Development Goals (SDGs) to foster equitable, sustainable, and culturally respectful music AI ecosystems [4, 5, 6, 13]. Aligned with our theme, “Sound, Music: Space, Place,” it preserves cultural “place” (local musical identities) and creative “space” (authorship, agency, equity) through a structured approach combining philosophical foundations, design principles, and practical solutions. Below, we detail the framework’s components, implementation roadmap, and case studies. 4.1 Philosophical Foundations East Asian philosophies provide a culturally sensitive ethical lens, complement-ing Western frameworks [6, 16]. Confucian harmony emphasizes collective well-being and social roles. This principle can be applied to formulate a set of guide-lines that guide AI to support communal creativity rather than focusing solely on individual gain [6, 16]. For instance, AI systems could prioritize collaborative workflows, ensuring artists’ contributions are valued within social contexts [14]. Buddhist ethics, rooted in interdependence and compassion, advocate minimizing harm, such as avoiding deepfakes that exploit artists’ identities [4, 6]. Daoist principles of ziran (natural spontaneity) and frugality promote environmentally conscious AI design, reducing computational waste [11, 16]. Shintoism’s animistic view, attributing spirit to non-human entities, encourages respectful use of AI as a creative partner [16]. These philosophies counter Eurocentric biases, ensuring music AI respects diverse cultural “places” [7, 8]. Technomoral virtues—honesty, empathy, civility, and flexibility—further ground the framework [4]. Honesty mandates transparency in AI processes, such as disclosing training data sources [11]. Empathy ensures AI designs consider artists’ emotional and Proc. of the 17th International Symposium on CMMR, London, UK, Nov. 3-7, 2025 703 Ethical Frameworks for Responsible Music AI cultural stakes, while civility promotes inclusive access for marginalized communities [4, 5]. Flexibility allows adaptation to evolving cultural and technological contexts, aligning with pluralistic ethics [6, 16]. 4.2 Design Principles Drawing on HCAI principles, the framework prioritizes user empowerment, transparency, and ethical alignment [13, 17]. Shneiderman’s HCAI model emphasizes amplifying human creativity through interactive, explainable systems [13]. For music AI, this translates to: − User Control: Artists retain authority over AI outputs, with tools offering overrides, previews, and editable suggestions [13, 14]. For example, DAWs like Logic Pro could integrate AI with customizable parameters [14]. − Transparency: Systems must disclose algorithms, data sources, and decision logic, enabling trust and accountability [2, 11]. Nayar’s transparency pledges for Suno AI serve as a model [2]. − Ethical Data Use: Training datasets must respect copyright and cultural consent, adhering to FAIR Data Principles [17]. Community consultation, as Huang et al. advocate, prevents appropriation [6, 16]. − Inclusive Design: AI tools should accommodate diverse musical traditions and accessibility needs, incorporating non-Western scales (e.g., raga, maqam) and supporting disabled users [7, 14]. UN SDGs, particularly SDG 4 (education), SDG 10 (reduced inequalities), and SDG 13 (climate action), guide equitable access, cultural inclusion, and sustainability [5]. This ensures music AI supports global creative ecosystems without exacerbating disparities [5, 15] 4.3 Practical Solutions To operationalize the framework, we propose actionable solutions addressing each risk from Section 3: − Blockchain-Based Attribution: Blockchain-based attribution systems can clarify ownership by recording creator contributions on a decentralized ledger, ensuring transparency and reducing disputes over royalties, as demonstrated by Li’s implementation of Hyperledger Fabric for music copyright protection [3,19]. Initiatives like Imogen Heap's Mycelia provide a practical example of how blockchain can empower artists by managing data and revenue streams directly. Smart contracts could allocate shares to human creators, AI developers, and rights holders, resolving disputes like those involving Suno AI [2]. This preserves creative “space” by clarifying authorship [3, 5]. − Community-Governed Datasets: Datasets must be co-curated with cultural stakeholders, such as Irish traditional music communities, to include diverse traditions (e.g., gamelan, kora, Irish jigs) and avoid appropriation [6, 7, 16, 20, 25]. Huang et al. advocate for non-hierarchical collaboration with ITM practitioners to respect Proc. of the 17th International Symposium on CMMR, London, UK, Nov. 3-7, 2025 704 S. Sankaran their values of authenticity and history, ensuring AI contributes to, rather than exploits, musical ecosystems [25]. Wang et al.’s adapted model, incorporating 45% non-Western elements, demonstrates improved resonance, while Kanhov et al. emphasize mutual engagement with communities of practice to ensure cultural integrity [7, 20]. Community governance, as per Huang et al., ensures consent and authenticity [6]. − Culturally Sensitive Design: AI systems should integrate non-Western scales, rhythms, and instruments, with safeguards against misuse of sacred music [6, 16]. Hantrakul’s exclusion of guqin in Tone Transfer exemplifies this approach [16]. Developers must collaborate with local musicians to maintain cultural “place” [6, 14]. Huang et al. advocate involving cultural practitioners to ensure ethical design, complementing Nayar’s stakeholder collaboration model [6, 16]. Fox et al. emphasize that AI tools like Amper and Jukedeck often fail to capture cultural emotional depth, underscoring the need for culturally informed design [22]. − Cognitive Model Integration: To address Rohrmeier’s cognitive challenge, AI systems should incorporate models of music cognition, capturing human perception, harmonic inference, and cultural-specific structures like non-Western rhythms [23]. This reduces Eurocentric bias and misrepresentation by aligning AI outputs with human musical experience, complementing diverse dataset curation [7, 17, 23]. − Transparency Standards: Metadata tagging, as Nayar suggests, embeds copyright signals in audio files to detect unauthorized use [2]. Public disclosure of training data sources and algorithmic logic aligns with Oğul’s transparency principles [11]. This fosters trust and accountability [13, 14]. − Bias Mitigation: Diverse datasets and regular audits, guided by FAIR principles, reduce Eurocentric bias [7, 17]. Mehta et al.’s call for inclusive data sourcing supports this, ensuring equitable representation [8]. Synthetic listeners, as in Wang et al., can evaluate cultural resonance [7]. − Agency Preservation: AI tools should act as “second ears,” offering suggestions while deferring to human intent [14, 13]. Newman et al.’s findings highlight musicians’ preference for collaborative AI, preserving artistic identity [14]. Customizable interfaces enhance creative “space” [13]. − Environmental Accountability: Energy-efficient algorithms and renewable-powered servers address AI’s carbon footprint [11, 16]. Daoist frugality, as Huang et al. note, guides sustainable design [16]. Industry standards for green AI, absent in current guidelines, are critical [5, 11]. − Economic Equity Mechanisms: Revenue-sharing models, artist cooperatives, and commons-based mechanisms like levy-based trust funds or musician ownership funds can counter the value gap, redistributing value to musical communities [5, 15, 24]. Clancy’s call for inclusive coalitions supports this, ensuring grassroots creators benefit [5]. Policy incentives for ethical AI adoption protect livelihoods [2, 5]. Williams and Barthet suggest [28] that new commercial opportunities for AI exist, such as IP licensing and compliance certifications, which can benefit creators. Proc. of the 17th International Symposium on CMMR, London, UK, Nov. 3-7, 2025 705 Ethical Frameworks for Responsible Music AI − Misuse Safeguards: Likeness thresholds, as Nayar proposes, define acceptable similarity to prevent deepfakes [2, 25]. Forensic musicology can detect unauthorized imitations [2, 4]. Huang et al. suggest that Buddhist ethics, prioritizing the relief of suffering, can guide AI design to minimize misuse, such as flooding soundscapes with AI-generated ‘spam’ that disrupts cultural ecosystems [6, 16, 25]. − Safety Protocols: Human-AI performance systems, like robotic ensembles, require fail-safes to prevent physical harm [4, 6]. Flick and Worrall’s safety guidelines for robotic art apply to music, ensuring safe creative “space” [4]. 4.4 Implementation Roadmap The framework’s implementation involves multi-stakeholder collaboration: 1. Policy Development: Governments and organizations (e.g., UNESCO, ISO, IEEE) should adapt OECD AI Principles for music, incorporating SDGs and East Asian ethics [11, 12, 6]. Legislation defining likeness thresholds and copyright reforms is urgent [2, 3]. 2. Industry Standards: Music AI developers must adopt transparency pledges and metadata tagging, as Nayar and Oğul suggest [2, 11]. Certifications for ethical AI, aligned with SDGs, incentivize compliance [5, 13]. 3. Community Engagement: Community Engagement: Artists, ethnomusicologists, and cultural leaders, including specialists in Irish traditional music as one example, should co-design datasets and tools, ensuring inclusivity [6, 7, 14, 20]. Workshops, as Newman et al. recommend, and sustained dialogues, as Kanhov et al. advocate, foster mutual feedback and respect for community values [14, 20]. 4. Education and Awareness: Training programs teach musicians to use AI as an expressive tool, preserving agency [1, 14]. Public campaigns raise awareness of AI’s cultural and economic impacts [5]. 5. Technical Innovation: Blockchain platforms, energy-efficient algorithms, and inclusive AI models require R&D investment [2, 11, 7]. Open-source tools enhance accessibility [14]. 4.5 Case Studies To illustrate the practical applications and potential impact of our proposed ethical framework, we examine three case studies of music AI systems that highlight key challenges and opportunities. These examples demonstrate how integrating East Asian philosophies, HCAI principles, and technomoral virtues can inform design choices, mitigate risks, and foster culturally respectful music AI ecosystems. The following case studies showcase diverse approaches to addressing ownership, cultural appropriation, and creator agency in music AI. − Suno AI: Nayar’s analysis shows Suno’s potential for user empowerment but highlights copyright disputes due to opaque training data [2]. Implementing blockchain Proc. of the 17th International Symposium on CMMR, London, UK, Nov. 3-7, 2025 706