Legislative Considerations for Generative Artificial Intelligence and Copyright Law

Who, if anyone, may claim copyright ownership of new content generated by a technology without direct human input? Who is or should be liable if content created with generative artificial intelligence infringes existing copyrights?

Innovations in artificial intelligence (AI) are raising new questions about how copyright law principles such as authorship, infringement, and fair use will apply to content created or used by AI. So-called “generative AI” computer programs—such as Open AI’s DALL-E and ChatGPT programs, Stability AI’s Stable Diffusion program, and Midjourney’s self-titled program—are able to generate new images, texts, and other content (or “outputs”) in response to a user’s textual prompts (or “inputs”). These generative AI programs are trained to generate such outputs partly by exposing them to large quantities of existing works such as writings, photos, paintings, and other artworks. This Legal Sidebar explores questions that courts and the U.S. Copyright Office have begun to confront regarding whether generative AI outputs may be copyrighted and how generative AI might infringe copyrights in other works.

Make sure to read the full paper titled Generative Artificial Intelligence and Copyright Law by Christopher T. Zirpoli for the Congressional Research Service at https://crsreports.congress.gov/product/pdf/LSB/LSB10922/5

The increasing use of generative AI challenges existing legal frameworks around content creation, ownership, and attribution. It reminds me of the time streaming began to challenge the – then – common practice of downloading copyrighted and user-generated content. How should legislators and lawmakers view generative AI when passing new regulations? 

Copyright, in simple terms, is a type of legal monopoly afforded to the creator or author. It is designed to allow the creator to monetize from their original works of authorship to sustain a living and continue to create because it is assumed that original works of authorship further society and expand knowledge of our culture. The current text of the Copyright Act does not explicitly define who or what can be an author. However, both the U.S. Copyright Office and the judiciary have afforded copyrights only to original works created by a human being. In line with this narrow interpretation of the legislative background, Courts have denied copyright for selfie photos created by a monkey arguing only humans need copyright as a creative incentive.

This argument does imply human creativity is linked to the possibility of reaping economic benefits. In an excellent paper titled “The Concept of Authorship in Comparative Copyright Law”, the faculty director of Columbia’s Kernochan Center for Law, Media, and the Arts, Jane C. Ginsburg refutes this position as a mere byproduct of necessity. Arguably, a legislative scope centered around compensation for creating original works of authorship is failing to incentivize creators and authorship altogether, who, for example, seek intellectual freedom and cultural liberty. This leaves us with a creator or author of a copyrightable work can only be a human. 

Perhaps, generative AI could be considered a collaborative partner used to create original works through an iterative process. Therefore creating an original work of authorship as a result that could be copyrighted by the human prompting the machine. Such cases would also fall outside of current copyright laws and not be eligible for protection. The crucial argument is the expressive element of a creative work must be determined and generated by a human, not an algorithm. In other words, merely coming up with clever prompts to allow generative AI to perform an action, iterating the result with more clever prompts, and claiming copyright for the end result has no legal basis as the expressive elements were within the control of the generative AI module rather than the human. The interpretation of control over the expressive elements of creative work, in the context of machine learning and autonomous, generative AI, is an ongoing debate and likely see more clarification by the legislative and judicial systems.    

To further play out this “Gedankenexperiment” of authorship of content created by generative AI, who would (or should) own such rights? Is the individual who is writing and creating prompts, who is essentially defining and limiting parameters for the generative AI system to perform the task, eligible to claim copyright for the generated result? Is the Software Engineer overseeing the underlying algorithm eligible to claim copyright? Is the company owning the code-work product eligible to claim copyright? Based on the earlier view about expressive elements, it would be feasible to see mere “prompting” as an ineligible action to claim copyright. Likewise, an engineer writing software code performs a specific task to solve a technical problem. Here, an algorithm leveraging training data to create similar, new works. The engineer is not involved or can be attributed to the result of an individual using the product to the extent that it would allow the engineer to exert creative control. Companies may be able to clarify copyright ownership through its terms of service or contractual agreements. However, a lack of judicial and legal commentary on the specific issue leaves it unresolved, or with few clear guidances, as of October 2023.     

The most contentious element of generative AI and copyrighted works is the liability around infringements. OpenAI is facing multiple class-action lawsuits over its allegedly unlicensed use of copyrighted works to train its generative models. Meta Platforms, the owner of Facebook, Instagram, and WhatsApp, is facing multiple class-action lawsuits over the training data used for its large-language model “LLaMA”. Much like the author of this paper, I couldn’t possibly shed light on this complex issue with a simple blog post, but lawmakers can take meaningful action. 

Considerations and takeaways for lawmakers and professionals overseeing the company policies that govern generative AI and creative works are: (1) clearly define whether generative AI can create copyrightable works, (2) exercise clarity over authorship and ownership of the generated result, and (3) outline the requirements of licensing, if any, for proprietary training data used for neural networks and generative modules.

The author looked at one example in particular, which concerns the viral AI-song “Heart On My Sleeve” published by TikTok user ghostwriter977. The song uses generative AI to emulate the style, sound, and likeness of pop stars Drake and The Weeknd to appear real and authentic. The music industry understandably is put on guard with revenue-creating content generated within seconds. I couldn’t make up my mind about it, so here you listen for yourself. 

The Problem With Too Much Privacy

The debate around data protection and privacy is often portrayed as a race towards complete secrecy. The author of this research paper argues that instead, we need to strike a balance between protection against harmful surveillance and doxing on one side and safety, health, access, and freedom of expression on the other side.

Privacy rights are fundamental rights to protect individuals against harmful surveillance and public disclosure of personal information. We rightfully fear surveillance when it is designed to use our personal information in harmful ways. Yet a default assumption that data collection is harmful is simply misguided. Moreover, privacy—and its pervasive offshoot, the NDA—has also at times evolved to shield the powerful and rich against the public’s right to know. Law and policy should focus on regulating misuse and uneven collection and data sharing rather than wholesale bans on collection. Privacy is just one of our democratic society’s many values, and prohibiting safe and equitable data collection can conflict with other equally valuable social goals. While we have always faced difficult choices between competing values—safety, health, access, freedom of expression and equality—advances in technology may also include pathways to better balance individual interests with the public good. Privileging privacy, instead of openly acknowledging the need to balance privacy with fuller and representative data collection, obscures the many ways in which data is a public good. Too much privacy—just like too little privacy—can undermine the ways we can use information for progressive change. Even now, with regard to the right to abortion, the legal debates around reproductive justice reveal privacy’s weakness. A more positive discourse about equality, health, bodily integrity, economic rights, and self-determination would move us beyond the limited and sometimes distorted debates about how technological advances threaten individual privacy rights.

Make sure to read the full paper titled The Problem With Too Much Data Privacy by Orly Lobel at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4578023

The United States has historically frayed away from enacting privacy laws or recognizing individual privacy rights. Arguably, this allowed the United States to innovate ruthlessly and progress society relentlessly. The Fourth Amendment and landmark Supreme Court cases shaped contemporary privacy rights in the U.S. over the past half of the century. In the aftermath of the September 11, 2001 terrorist attacks, however, privacy rights were hollowed out when H.R. 3162 aka the Patriot Act was passed, which drastically expanded the Government’s surveillance authority. In 2013, whistleblower Edward Snowden released top-secret NSA documents to raise the public’s awareness of the scope of surveillance and invasion of privacy done to American citizens and citizens of the world by and large. In 2016, the European Union adopted regulation EU 2016/679 aka General Data Protection Regulation (GDPR). Academic experts who participated in the formulation of the GDPR wrote that the law “is the most consequential regulatory development in information policy in a generation. The GDPR brings personal data into a complex and protective regulatory regime.” This kickstarted a mass adoption of privacy laws across different States from California’s Consumer Protection Act of 2018 (CCPA) to Virginia’s Consumer Data Protection Act of 2021 (VCDPA). 

History, with all its legislative back-and-forth evolutions, illustrates the struggle around balancing data privacy with data access. Against this backdrop, the author argues that data is information and information is a public good. Too much privacy restricts, hampers, and harms access to information and therefore innovation. And, while society has always faced difficult choices between competing values, modern technology has the capability to effectively anonymize and securely process data, which can uphold individual privacy rights while supporting progressive change.   

Find A Behavioral Solution To Your Product Design Problem

Our actions are (very much) predictable and can be influenced.

Humans are complicated. Humans are different. Humans are irrational, unpredictable, and emotional. In DECODING the WHY – How Behavioral Science is Driving the Next Generation of Product Design author Nate Andorsky embraces all these idiosyncrasies by answering these underlying questions: what makes us do what we do and how can product designers learn from these behavioral patterns to build better products. 

Andorsky takes the reader on a story-driven adventure into behavioral science. Decoding the Why lives in a constant tension between the evolution of product design and human behavior. It describes psychological concepts and how they influence product designs. It provides practical guidance on how to meet the consumer’s cognitive state before intent is formed and how to use behavioral science to nudge the consumer towards action. For example in the part about ‘Meeting Our Future Selves’ Andorsky reviews Matthew McConaughey’s iconic Oscar acceptance speech after winning the Oscar for his performance in Dallas Buyers Club.

“When I was 15 years old I had a very important person in my life come to me and say, ‘Who’s your hero?’ I said, ‘I don’t know, I gotta think about that, give me a couple of weeks.’

This Person comes back two weeks later and says, ‘Who’s your hero?’ I replied, ‘You know what, I thought about it and it’s me in ten years.’

So I turn twenty-five. Ten years later, that same person comes to me and says, ‘So are you a hero?’ I replied, ‘No, no, no, not even close.’ ‘Why?’ she said. ‘Cause my hero is me at thirty-five,’ I said.

See, every day, every week, every month, every year of my life, my hero is always ten years away. I’m never going to meet my hero, I am never going to obtain that, and that’s totally fine because it gives me somebody to keep on chasing.”

If humans were rational we’d all pursue the rational thing to maximize our time and energy. However, we are not rational. All too often we give in to the instant gratification that lies in the moment by putting off the thing that helps us tomorrow. This concept is also known as Hyperbolic Discounting. Andorsky walks the reader through the obstacles that keep us from meeting our future selves by reviewing methods such as reward systems, gamification models, commitment devices, and goal setting, all of which, are used to inform product design. 

If I ever write a book, I will likely attempt to create a similar structure and flow. Andorsky did an excellent job by breaking down the content into easily digestible parts. Each part tells a captivating story concluding in an engaging question for the reader. While the subject matter could have easily been told with jargon and psychology terminology, the author consistently uses clear and non-academic language to explain a variety of behavioral and psychological concepts and theories. Altogether this makes for an accessible page-turner offering a wide range of practical applications. 

Taking a birds-eye view on Decoding the Why, I feel, I could come to two conclusions that could not be further apart: (1) Andorsky answers the eternal question of what makes us do what we do and how product designers can learn from these behavioral patterns to build better products or (2) Andorsky provides ammunition to weaponize psychology in order to calibrate intrusive technology that can be used to manipulate and exploit human behavior. Whatever your position is on the question of using behavioral science to influence user behavior, this book is a gateway to explore psychological concepts, and it is an important read for changemakers. It can be used for good, or, it can be used to inform better public policy. I’d rank Decoding the Why as a must-read for product designers, product managers, and anyone working to improve user experiences in technology.