The Ethical and Practical Challenges of Generating Culturally Specific Popular Music with AI: A Focus on Hinglish

Core Concepts

This paper proposes a research project exploring the use of AI to generate popular songs in Hinglish, a mixcode language common in South Asia, while acknowledging the significant ethical and practical challenges inherent in such an endeavor.

Abstract

Bibliographic Information:

Kashik, A., & Rush, K. (2024). Generating Mixcode Popular Songs with Artificial Intelligence: Concepts, Plans, and Speculations. AIMC 2024 (09/09 - 11/09 ). Retrieved from https://aimc2024.pubpub.org/pub/rdulfbve

Research Objective:

This paper outlines a proposed research project investigating the potential of AI to generate culturally specific popular music, focusing on Hinglish, a mixcode language prevalent in South Asia. The central research question is: How can AI be used to generate mixcode Hinglish songs that are validated as successful within their specific cultural, social, musical, and linguistic context?

Methodology:

The project proposes a three-pronged approach:

1. Lyrics generation: Utilizing natural language processing techniques on Hinglish social media text to train a model for generating lyrics based on specific intents.
2. Melody generation: Exploring various AI models for music generation, considering the complexities of matching melody to Hinglish lyrics and incorporating culturally relevant musical elements.
3. Validation processes: Employing a combination of human and machine learning evaluations at each stage, including feedback from Hinglish speakers and music experts, to assess the success of the AI-generated songs.

Key Findings:

As this paper presents a project proposal, concrete findings are not yet available. However, the authors anticipate significant challenges and potential contributions to knowledge in areas such as:

• Developing culturally sensitive AI models for music generation.
• Addressing ethical concerns related to bias, copyright, and the impact of AI on artistic expression.
• Understanding the complexities of human validation in the context of culturally specific music.

Main Conclusions:

The authors argue for the potential of AI in generating culturally specific popular music while acknowledging the ethical and practical complexities involved. They emphasize the need for a culturally sensitive and iterative approach, incorporating human feedback and addressing potential biases.

Significance:

This research contributes to the growing field of AI music generation by focusing on the under-explored area of mixcode languages and culturally specific music. It highlights the importance of ethical considerations and stakeholder engagement in developing AI tools for creative applications.

Limitations and Future Research:

The project is in its initial stages and faces challenges related to data collection, model development, and human validation. Future research will involve refining the proposed methodology, addressing ethical concerns, and evaluating the impact of the AI-generated music.

Stats

The SAARC countries account for approximately 25% of the global population.
The SAARC countries account for 33.4% of the global low-income population.

Quotes

"Generative AI presents a potent challenge to the traditional ethnomusicological definition of music as ‘humanly organized sound’ (Blacking 1973)."
"Music learning, particularly within ‘intermusical’ (i.e., multi- or intercultural) contexts, is a multi-sensory embodied process (van den Dool 2016), one that is difficult to replicate with AI, at least at this stage of our knowledge."
"Hinglish is widely spoken in South Asian countries such as India, Pakistan, Nepal, and Bangladesh for communication, education, marketing, and election campaigns."

Key Insights Distilled From

Generating Mixcode Popular Songs with Artificial Intelligence: Concepts, Plans, and Speculations

by Abhishek Kau... at arxiv.org 11-12-2024

https://arxiv.org/pdf/2411.06420.pdf

Deeper Inquiries

How might the increasing accessibility of AI music generation tools impact the cultural landscape of popular music globally?

Answer 1 here:
The increasing accessibility of AI music generation tools has the potential to significantly impact the cultural landscape of popular music globally, both positively and negatively.
Potential Positive Impacts:

Democratization of Music Production: AI tools can empower individuals with limited musical training to create and share their music, potentially diversifying the soundscape of popular music beyond established industry gatekeepers. This could lead to a surge in new genres and styles, reflecting a broader range of cultural influences and personal expression.
Cross-Cultural Collaboration and Fusion: AI can facilitate collaboration between artists from different cultures and backgrounds by bridging language barriers and musical styles. This could lead to exciting new fusions and hybrid genres, enriching the global musical landscape.
Personalized Music Experiences: AI algorithms can analyze individual listening habits and preferences to generate customized music tailored to specific tastes. This could lead to a more engaging and personalized listening experience for consumers.
Potential Negative Impacts:

Homogenization of Music: If AI tools are trained on dominant musical styles and trends, they may perpetuate existing power imbalances and lead to a homogenization of sound, potentially marginalizing less-represented musical cultures and traditions.
Diminished Value of Human Creativity: The widespread use of AI in music production raises concerns about the devaluation of human creativity and musicianship. If listeners perceive AI-generated music as less authentic or meaningful, it could impact the livelihoods of human musicians and songwriters.
Ethical Concerns and Bias: AI models are susceptible to biases present in the data they are trained on. This could lead to the perpetuation of harmful stereotypes and cultural appropriation if not addressed carefully.
Mitigating Negative Impacts:
To ensure that AI music generation tools contribute positively to the cultural landscape of popular music, it is crucial to:

Promote Diversity and Inclusion: Develop and train AI models on diverse datasets representing a wide range of musical cultures, genres, and styles.
Emphasize Ethical Considerations: Implement guidelines and regulations to address copyright issues, cultural appropriation, and the responsible use of AI in music production.
Support Human-AI Collaboration: Encourage the use of AI as a tool to augment and enhance human creativity, rather than replacing it entirely.
The future of popular music in the age of AI will depend on how we navigate these opportunities and challenges, ensuring that technology serves as a force for cultural enrichment and artistic expression.

Could the focus on creating AI-generated music that is validated as "successful" within a specific cultural context inadvertently stifle artistic innovation and experimentation?

Answer 2 here:
Yes, the focus on creating AI-generated music validated as "successful" within a specific cultural context could inadvertently stifle artistic innovation and experimentation. This is because:

Reinforcement of Existing Norms: Validating AI-generated music based on its adherence to established cultural norms and expectations could create a feedback loop that reinforces those norms, potentially discouraging deviations from the status quo.
Limited Exposure to Unconventional Sounds: If AI models are primarily trained on music already deemed successful within a specific cultural context, they may not be equipped to generate or appreciate sounds that challenge those norms or push creative boundaries.
Subjectivity of "Success": The definition of "successful" music is inherently subjective and can vary widely within and across cultures. Relying solely on pre-determined metrics of success could limit the exploration of new sonic possibilities and artistic expressions that defy easy categorization.
However, this risk can be mitigated by:

Embracing Experimentation: Encourage the development of AI models that prioritize experimentation and exploration of unconventional sounds, even if they don't immediately align with established notions of success.
Diversifying Training Data: Train AI models on a wide range of musical data, including music from marginalized communities, experimental genres, and historical periods, to expose them to a broader spectrum of sonic possibilities.
Human-in-the-Loop Approach: Integrate human feedback and evaluation throughout the AI music generation process, allowing for subjective interpretations and artistic input to shape the creative output.
The goal should be to leverage AI as a tool for expanding the boundaries of musical creativity, not confining it. By embracing experimentation, diversity, and human collaboration, we can ensure that AI-generated music contributes to a vibrant and ever-evolving cultural landscape.

What are the potential implications of using AI to generate music for social good, and how can we ensure that such tools are used responsibly and ethically?

Answer 3 here:
Using AI to generate music for social good holds immense potential, but it's crucial to proceed responsibly and ethically.
Potential Benefits:

Amplifying Marginalized Voices: AI can empower communities and individuals who may lack access to traditional music production resources to create and share music that promotes their causes and stories.
Raising Awareness for Social Issues: AI-generated music can be a powerful tool for raising awareness about critical social issues, such as climate change, poverty, and inequality, by crafting emotionally resonant and culturally relevant messages.
Facilitating Healing and Well-being: Music has therapeutic benefits, and AI can be used to create personalized music experiences that promote mental health, emotional well-being, and social cohesion.
Potential Risks:

Misrepresentation and Cultural Appropriation: If not developed and deployed sensitively, AI-generated music for social good could perpetuate harmful stereotypes or appropriate cultural elements in insensitive ways.
Manipulation and Propaganda: The emotional power of music makes it susceptible to manipulation. AI-generated music could be used to spread misinformation or promote harmful ideologies under the guise of social good.
Exacerbating Existing Inequalities: Unequal access to AI technology and resources could exacerbate existing social and economic disparities, potentially marginalizing communities that could benefit most from these tools.
Ensuring Responsible and Ethical Use:

Community Engagement and Collaboration: Involve stakeholders, particularly from marginalized communities, in every stage of development and deployment of AI tools for social good, ensuring that their voices are heard and their needs are met.
Transparency and Accountability: Develop transparent and accountable mechanisms for creating and disseminating AI-generated music, clearly outlining the intentions, methodologies, and potential impacts of such projects.
Ethical Guidelines and Regulations: Establish clear ethical guidelines and regulations for using AI in music for social good, addressing issues such as cultural sensitivity, data privacy, and potential biases.
By prioritizing ethical considerations, community engagement, and responsible development, we can harness the power of AI to create music that inspires positive social change and contributes to a more just and equitable world.