Fair Use Of Generative AI: Why Its Plausibility Is Questionable

Sri Bhargav Krishna Adusumilli, Co-Founder of Mindquest Technology Solutions, is an expert in AI, blockchain and IoT security.

Generative AI has emerged as a transformative force in technology, creating text, art, music and code that can rival human efforts. However, its rise has sparked significant debates around copyright law, particularly regarding the concept of fair use.

While fair use—a legal framework allowing limited use of copyrighted material without permission—has long been a pillar of creativity and innovation, applying it to generative AI is fraught with legal and ethical challenges.

Here’s why the fair use argument for generative AI is largely implausible.

Fair Use Defined

Fair use in U.S. copyright law considers four key factors to determine whether copyrighted content can be used without permission:

1. Purpose And Character of Use: Is the use transformative, adding new meaning or merely a reproduction?

2. Nature Of The Copyrighted Work: Is the work creative or factual?

3. Amount And Substantiality Used: How much of the original work is used, and is it the essential part?

4. Effect On The Market For Or Value Of The Work: Does the use harm the market value of the original work?

While these factors have worked well in traditional scenarios like criticism, parody or education, generative AI presents unique challenges that stretch these boundaries.

Generative AI And Copyrighted Material

Generative AI models are trained on vast datasets, often containing copyrighted materials scraped from the internet, including books, articles, music and art. These models don’t explicitly store this content but learn patterns and structures, enabling them to generate outputs that may closely mimic or resemble the training data.

For example:

• An AI art generator might create an image resembling a copyrighted painting.

• A text generator could produce content strikingly similar to an existing article.

Does this process fall under fair use? The answer depends on whether the AI’s use of copyrighted material satisfies the fair use criteria, and in most cases, it does not.

Why Fair Use Is Difficult To Argue For Generative AI

1. The Transformative Use Problem

Fair use relies heavily on whether the use is transformative, meaning it adds new meaning, value or purpose to the original work. While human creativity often achieves this through intentionality—commentary, critique or parody—AI outputs rarely meet this standard.

For example:

• An AI-generated artwork blending styles from multiple creators may appear novel but lacks the purposeful transformation of human creativity.

• AI-generated text might reorganize or paraphrase existing content without offering unique insights or value.

AI lacks the intent to create something transformative, making it challenging to meet this critical fair use requirement.

2. The Scale Of Usage

Generative AI models are trained on massive datasets, often containing millions of works. While individual pieces may contribute minimally, the sheer scale of usage complicates the argument for fair use. Fair use traditionally applies to specific, limited uses—not wholesale ingestion of copyrighted content on a global scale.

3. Market Impact

One of the most significant fair use factors is the effect on the market for the original work. Generative AI threatens to disrupt creative markets by producing high-quality content at scale.

For instance:

• AI-generated art could compete directly with human artists, reducing demand for commissions.

• Automated writing tools might undercut opportunities for professional writers.

When AI-generated content competes with human creators, courts are unlikely to view its use of copyrighted material as fair.

4. Lack Of Consent

Most datasets used to train generative AI models include copyrighted materials without the creators’ consent. This lack of permission further weakens any fair use claim. Creators have the right to control how their work is used, and the absence of their consent undermines ethical and legal defenses.

The Legal Landscape

Several legal cases are already testing the boundaries of fair use in generative AI:

• Authors Guild v. Google (2015): Google’s use of books for indexing and snippet display was deemed transformative and fair use. However, the context was limited to indexing, not generating entirely new content.

• Anderson v. Stability AI Ltd. (2024): Artists alleged that AI art generators infringed their copyrights by training on their works without consent, highlighting the tension between fair use and copyright protections.

These cases underscore the difficulty of applying traditional fair use principles to generative AI’s large-scale, automated processes.

Ethical Considerations

Even if some uses of generative AI were deemed legal under fair use, ethical concerns remain. Should creators have the right to opt out of having their works used in AI training datasets? Should AI companies share profits with the creators whose works were used for training? These questions highlight the broader moral implications of AI’s reliance on copyrighted material.

Proposed Solutions

The current legal and ethical frameworks are ill-equipped to address the challenges posed by generative AI. Moving forward, several measures could help strike a balance between innovation and creator rights:

1. Opt-In Models: Creators should be able to voluntarily include their works in training datasets with appropriate compensation or recognition.

2. Licensing Systems: AI companies could negotiate licensing agreements similar to music licensing models.

3. Transparency Requirements: Companies should disclose the datasets used for training, fostering accountability.

4. Regulatory Guidelines: Governments should establish clear rules to protect creators while enabling technological progress.

Looking Ahead

The fair use doctrine was designed for specific, limited scenarios—not for the large-scale, automated consumption of copyrighted material by generative AI. While the technology holds immense potential, its current reliance on copyrighted works without permission makes fair use a weak defense.

To ensure generative AI serves society without undermining creators, we need new legal and ethical frameworks that address these challenges head-on. Only by evolving beyond traditional fair use can we strike a balance between innovation and protecting the rights of those who fuel creativity.

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