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Generative artificial intelligence has been hailed for its , and especially by lowering the . While the has often been highlighted, the popularity of these tools poses questions about intellectual property and copyright protection.

Generative AI tools such as ChatGPT are powered by , or AI models . Generative AI is billions of pieces of data taken from text or images scraped from the internet.

Generative AI uses very powerful machine learning methods such as and on such vast repositories of data to understand the relationships among those pieces of data – for instance, which words tend to follow other words. This allows generative AI to perform a broad range of tasks that can .

One problem is that output from an AI tool can be . Leaving aside how generative models are trained, the challenge that widespread use of generative AI poses is how individuals and companies could be held liable when generative AI outputs infringe on copyright protections.

When prompts result in copyright violations

and have raised the possibility that through selective prompting strategies, people can end up creating text, images or video that violates copyright law. Typically, generative AI tools output an image, text or video but . This raises the question of how to ensure that users of generative AI tools do not unknowingly end up infringing copyright protection.

The legal argument advanced by generative AI companies is that AI trained on copyrighted works is not an infringement of copyright ; rather, they are designed to learn the associations between the elements of writings and images like words and pixels. AI companies, including Stability AI, maker of image generator Stable Diffusion, contend that output images provided in response to a particular text prompt for any specific image in the training data.

Builders of generative AI tools have argued that prompts do not reproduce the training data, which should protect them from claims of copyright violation. Some audit studies have shown, though, that can issue by producing works that .

Establishing infringement requires between expressive elements of a stylistically similar work and original expression in particular works by that artist. Researchers have shown that methods such as , which involve selective prompting strategies, and , which tricks generative AI systems into revealing training data, can recover individual training examples ranging from photographs of individuals to trademarked company logos.

Audit studies such as the one provide several examples where there can be little ambiguity about the degree to which visual generative AI models produce images that infringe on copyright protection. The New York Times provided a similar comparison of images showing how generative AI tools .

How to build guardrails

Legal scholars have dubbed the challenge in developing guardrails against copyright infringement into AI tools .” The more a copyrighted work is protecting a likeness – for example, the cartoon character Snoopy – the more likely it is a generative AI tool will copy it compared to copying a specific image.

Researchers in computer vision of how to detect copyright infringement, such as logos that are counterfeited or . Researchers have also examined how . These methods can be helpful in detecting violations of copyright. Methods to could be helpful as well.

With respect to model training, AI researchers have suggested methods for making . Some AI companies such as to not use data produced by their customers to train advanced models such as Anthropic’s large language model Claude. Methods for AI safety such as – attempts to force AI tools to misbehave – or ensuring that the model training process between the outputs of generative AI and copyrighted material may help as well.

Role for regulation

Human creators know to decline requests to produce content that violates copyright. Can AI companies build similar guardrails into generative AI?

There’s no established approaches to build such guardrails into generative AI, nor are there any to establish copyright infringement. Even if tools like these were available, they could put an excessive burden on .

Given that naive users can’t be expected to learn and follow best practices to avoid infringing copyrighted material, there are roles for policymakers and regulation. It may take a combination of legal and regulatory guidelines to ensure best practices for copyright safety.

For example, companies that build generative AI models could to limit copyright infringement. Similarly, regulatory intervention may be necessary to ensure that builders of generative AI models in ways that reduce the risk that the output of their products infringe creators’ copyrights.

Anjana Susarla receives funding from the ³Ô¹ÏÍøÕ¾ Institute of Health