Navigating Copyright Challenges in AI-driven Art & Media


Rootcode AI

December 08 2023

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The rapid growth of Generative AI has ushered in a new era of creativity and content production, bringing both opportunities and challenges. Generative AI models are trained using vast amounts of data sourced from the internet, enabling them to capture a comprehensive understanding of the content they are trained on. After fine-tuning, they become adept at creating novel content based on the knowledge learned through extensive training. The diversity of Generative AI models are notable, each specializing based on its training data. For instance, Large Language Models like GPT-4, Claude, and Llama, trained on textual data, excel in generating written content. Similarly, models trained on visual data like DALL-E and Imagen can create images from textual prompts, while those trained on musical data (Lyria by Deepmind) can compose new melodies. Most of these models are now easily accessible through user-friendly applications like ChatGPT, skyrocketing their usability among general consumers.

While this provides an exciting opportunity for creatives and businesses alike to experiment and produce creative content rapidly, it also raises questions about the ownership of the content created. As a result, companies leveraging generative AI are increasingly involved in navigating complex copyright issues, particularly in media, music, visual arts, and entertainment. This article delves into the landscape of copyright challenges in different industries and potential solutions deployed by both Generative AI vendors and businesses to ensure fair and compliant usage of the transformative technology.

Copyright Concerns in Media and Content Creation

In media and content creation, businesses navigating the use of AI-generated content face significant copyright challenges. This primarily includes the risk of inadvertently infringing upon copyrights, a widespread issue in journalism, writing, and other content creation areas where unauthorized reuse or modification is common. Addressing these issues is crucial as they emphasize the need to protect the original creators’ rights and develop a balanced content utilization approach.

In response to these challenges, recent strategic partnerships between LLM providers and media organizations have been instrumental in establishing responsible and ethical practices for using AI-generated content. For instance, tech giants such as Google and OpenAI are in talks to forge partnerships with news organizations, allowing them to use archived news collections for training their large language models (LLMs). These partnerships are designed to ensure compliance with copyright regulations, benefiting the data owners (news organizations), LLM providers (like Google and OpenAI), and end-users.

Furthermore, LLM providers are also proactive in supporting their customers against potential legal issues. A recent example is the OpenAI “Copyright Shield” program, introduced in Open AI Dev Day 2023, which offers comprehensive financial and legal support to customers facing legal challenges due to copyright violations from using OpenAI’s models. This initiative is a significant step in ensuring that businesses can leverage generative AI technologies while mitigating the risks associated with copyright infringement.

In addition to forming strategic alliances, there is a growing focus on creating robust internal policies and implementing technological solutions to mitigate copyright concerns. Adobe, for example, pays royalties to content creators who provide data to train its generative AI tool, Adobe Firefly. Similarly, NVIDIA unveiled its own generative AI service called Picasso, which uses data from sites such as Getty Images and Shutterstock and plans to pay royalties contributing to the original content creators.

Copyright Complexities in the Music Industry

The music industry’s landscape is undergoing a significant transformation with the advent of generative AI. For example, the recently released music generation model from Google Deepmind (“Lyria”) can be used to generate different musical compositions based on natural language inputs from the user.

This intensifies the longstanding challenges of copyright in the music industry. Historical issues like unauthorized sampling and cover song disputes have evolved, demanding novel approaches to protect musicians’ intellectual property. This evolution has led to pivotal legal precedents, shaping how existing musical works are utilized. A compelling example of the complexities involved emerged when a TikTok user used AI to create a song mimicking the styles of Drake and The Weeknd. The “Heart on My Sleeve” track quickly went viral on platforms like TikTok and Spotify. However, it soon faced takedown actions by Universal Music Group, sparking a debate about the copyright implications of AI-generated music that closely resembles the work of established artists.

On the other hand, the industry is also seeing transformative shifts from technology companies that develop Generative AI models. A notable development is the ongoing negotiations between tech giants like Google and music labels such as Universal Music, aiming to secure licenses for using artists’ voices and melodies in AI-generated songs. Also, Google has already partnered directly with artists like Charlie Puth and John Legend to develop the Lyria model. This potential collaboration shows the intricate balance needed between leveraging AI’s capabilities and respecting artists’ rights.

The ability of AI to emulate well-known artists’ voices also raises challenges about protecting copyright and artistic authenticity.

To address these challenges, the industry is exploring several solutions:

  • Attribution and Authorship Guidelines: Establishing clear guidelines for attribution is essential. This involves acknowledging the human input

  • Collaborative Frameworks: Creating partnerships between AI developers, artists, and music labels is crucial. These collaborations can facilitate the development of ethical guidelines and fair compensation models, ensuring that all parties involved in the AI music creation process are recognized and remunerated.

  • Digital watermarking: Music generation models like Lyria from Google have embedded watermarks in every note created, which are inaudible to humans but can be used to identify whether a piece of music is AI-generated or not. This avoids conflicting scenarios where it’s unclear to listeners whether a piece of music is human or AI-generated

  • Legal Adaptations: The industry needs to adapt its legal structures to address the unique challenges posed by AI. This might include revising copyright laws to account for AI’s role in the creative process and determining how existing laws apply to AI-generated works.

By focusing on these solutions, the music industry aims to create a balanced environment where the innovative capabilities of generative AI can be leveraged effectively while safeguarding the rights and interests of artists and other stakeholders.

Copyright Complexities in the Visual Artwork Industry

The use of AI in visual arts not only opens new creative avenues but also poses significant copyright challenges. Historically, the art world has seen numerous instances of unauthorized use of artwork. A classic example is the widespread replication of Leonardo da Vinci’s Mona Lisa, with countless copies made over centuries, often without proper attribution. The commercialization of generative AI tools like Midjourney and DALL-E has further intensified these pre-existing challenges around ownership in the art industry.

Below are some main challenges brought forward by generative AI to the art industry:

  • Royalties and recognition: The rise of AI-generated art leads to scenarios where original artists are not compensated for their work being used as training data for AI. For example, an artist’s unique style may be incorporated into a Generative model’s dataset, and the model then generates new works in that style, potentially reducing the demand for the original artist’s work and affecting their income without royalty or recognition.

  • Unintended copyright infringement: When a generative AI model generates a piece of art, it often does so by learning from a vast dataset that includes various artworks. The legal challenge here is significant: determining whether the model’s output is a new, independent creation or an unauthorized derivative of an existing work. This is complicated because AI doesn’t create in a vacuum; it relies on pre-existing data, leading to outputs that might inadvertently mirror copyrighted materials. Therefore, artists, copyright holders, and legal experts grapple with how to classify and protect these AI-generated works without stifling creativity and innovation in the digital art space.

  • Training data rights: When a generative AI model is trained on copyrighted artworks, it often uses these inputs to develop and refine its ability to create new content. The model builders will profit from the AI’s capabilities, while the artists who created the original works used in the training process do not receive compensation. This creates a significant imbalance, where the contributions of the original artists are utilized for commercial gain without due recognition or compensation. The recent lawsuit by Getty Images against Stability AI on copy infringement is an excellent example of this challenge model providers face.

To address the issue of unauthorized usage, the art industry is increasingly turning to advanced technologies such as Google’s AI-specific watermarking tool. This tool embeds invisible markers in digital artworks, ensuring the proper attribution of ownership. Additionally, AI’s ability to generate unique digital signatures has streamlined the process of verifying artwork's authenticity. However, efficiently implementing these verification tools poses a significant challenge, particularly given the rapid proliferation of AI-generated images. The effective deployment of such technologies is essential in fostering a sense of trust and credibility within the digital art space, which is now saturated with abundant visual content.


In conclusion, the rapid advancement of generative AI presents a paradigm shift in content creation, challenging traditional copyright norms across media, music, and visual arts industries. Solutions such as strategic partnerships between AI developers and content creators, advanced digital rights management systems, and legal adaptations are crucial in addressing these complexities.

Looking ahead, the continual advancement of technological solutions like advanced watermarking is crucial in maintaining the integrity of creative industries. These innovations should evolve in tandem with AI technologies to address the ever-changing landscape of content creation and distribution. Moreover, adopting an ethical framework that emphasizes transparency, fairness, and respect for intellectual property will be instrumental in democratizing the benefits of generative AI. This approach will protect the rights of creators and ensure a balanced and thriving environment for all stakeholders in the creative ecosystem.