Liability, Authorship, & Symmetrial Causation in AI-Generated Outputs

By: Jacob Alhadeff

Copyright has insufficiently analyzed causation for both authorship and liability because, until now, causation was relatively obvious. If someone creates a painting, then they caused the work and receive authorial rights. If it turned out that the painting was of Mickey Mouse, then that painter may be liable for an infringing reproduction. However, recent technological advances have challenged the element of causation in both authorship and infringement. In response, recent law and scholarship have begun to address these issues. However, because they have addressed causation in isolation, current analysis has provided logically or ethically insufficient answers. In other words, authorial causation has ignored potential implications for an entity’s infringement liability, and vice-versa. Regardless of how the law responds, generative AI will require copyright to explore and enumerate the previously assumed causation analyses for both infringement and authorship. This blog explores how generative AI exposes the logical inconsistencies that result from analyzing authorial causation without analyzing causation for infringing reproductions.

Generative AI largely requires the following process: (1) an original artist creates works, (2) a developer trains an AI model on these works, and (3) an end-user prompts the AI to generate an output, such as “a mouse in the style of Walt Disney.” This generative AI process presents a novel challenge for copyright in determining who or what caused the output because generative AI challenges conventional notions of creation.

Causing Infringement

Andersen et al. recently filed a complaint against Stability AI, one of the most popular text-to-art foundation models. This class action alleges that Stability AI is directly liable for infringing that result from end-user prompted generations. However, in a recent decision more closely analyzing causation and volition in infringement, the Ninth Circuit found that “direct liability must be premised on conduct that can reasonably be described as the direct cause of infringement.” Stability AI should not be found directly liable for infringing these artists’ copyright, in part because Stability AI cannot reasonably be said to be the direct cause of infringement. Such a finding would be similar to holding Google liable for reproducing images of Mickey Mouse on people’s computer screens when they search for “Mickey Mouse.”  

This lawsuit is particularly relevant since end-users have prompted thousands of generations that include the phrase “Mickey Mouse” and many appear substantially similar to Disney’s Mickey. If thousands of end-users have intentionally prompted the AI to generate Mickey Mouse, then what volitional conduct can most reasonably be described as the direct cause of infringement? It is clearly the end-user. However, what if the end-user simply prompted “a cartoon mouse” and the AI generated an infringing image of Mickey? Here, the end-user may not have intended to generate Mickey and reasonable notions of fairness may not find the end-user as the most direct cause of infringement. However, copyright is a strict liability tort, meaning that liability attaches regardless of a reproducer’s intent. Therefore, unless copyright applies an intentional or a negligence theory for direct liability, which it should not, then whomever or whatever is liable for infringing outputs shall be liable for both of the infringing outputs— “Mickey Mouse” and “a cartoon mouse.” Such an outcome not only feels deeply unfair, but it is unreasonable to say that the end-user is the direct cause of infringement when prompting “a cartoon mouse,” and vice versa. 

Cases called to answer similar questions have recently grappled with these same issues of volition and causation. Generally, courts have been hesitant to find companies liable for actions that are not reasonably deemed volitional conduct causing infringement. The court in Cartoon Network, for example, found that “volition is an important element of direct liability.” In the Loopnet case, the court found that “the Copyright Act… requires conduct by a person who causes in some meaningful way an infringement.” In this way, the law has so far mirrored our prior intuitions of fairness. Legal scholarship has noted that when copyright law has grappled with novel technology, it has found that causation in infringement requires volition that “can never be satisfied by machines.” This reasoning, as applied  to generative AI, may mean that an AI company should not normally be directly liable for outputs that infringe the reproduction right. 

Causing Authorship

This causation analysis has also begun for authorship rights. One copyright scholar compellingly argues that copyright law should explicitly enumerate a causal analysis for granting authorship rights. Such an analysis would follow tort law’s two step causation analysis including: (1) creation in fact and (2) legal creation. Aviv Gaon surveys authorial options in The Future of Copyright in the Age of AI, writing that there are those that favor assigning authorship to the end-user prompter, the AI developer, finding outputs joint works, or even attributing authorship to the AI itself. The simplest legal option would be to treat AI like a tool and grant authorship to the end-user. This is exactly how the law responded when photography challenged conventional notions of creativity and authorship. Opponents of finding photographers as authors argued that photography was “merely mechanical, with no place for… originality.” The Supreme Court in Burrow Giles instead found that the photographer “gives effect to the idea” and is the work’s “mastermind” deserving of copyright. 

However, treating AI like a conventional tool is an inconsistent oversimplification in the current context. Not only is it often less analogous to say that an end-user prompter is the ‘mastermind’ of the output, but AI presents a more attenuated causation analysis that should not result in  a copyright for all AI-generations. As an extreme example, recent AIs are employing other AIs as replicable agents. In these circumstances, a single prompt could catalyze one AI to automatically employ other AI agents to generate numerous potentially creative or infringing outputs. Here, the most closely linked human input would be a prompt that could not be said to have masterminded or caused the many resultant expressive outputs. Under Balganesh’s framework, no human could reasonably be found as the factual or legal cause of the output. Such use-cases will further challenge the law’s notions of foreseeability as reasonable causation becomes increasingly attenuated.

Importantly, in the face of this ongoing debate and scholarship, the Copyright Office recently made their determination on authorship for AI-generated works. In February 2023, the US Copyright Office amended its decision regarding Kristina Kashtanova’s comic book, Zarya of the Dawn, stating that the exclusively AI-generated content is not copyrightable.  Ms. Kashtanova created her comic book using Midjourney, a text-to-art AI, to generate much of the visual art involved. The copyright office stated that her “selection, coordination, and arrangement” of AI-generated images are copyrightable, but not the images themselves. The Office’s decision means that all exclusively AI-generated content, like natural phenomena, is not the type of content copyright protects and is freely accessible to all. The Office’s decision was based on their interpretation that “it was Midjourney—not Kashtanova—that originated the ‘traditional elements of authorship.’” The Office’s decision is appropriate policy, but when analyzed in conjunction with the current law on causation in infringement, it is inconsistent and may result in an asymmetrical allocation of the rights and duties that attend creation. Relevantly, how can a machine that is incapable of volition originate art? This is one of many ontological paradoxes that AI will present to law. 

Symmetrically Analyzing Causation

Two things are apparent. First, there is a beautiful symmetry in AI-generations being uncopyrightable, and the machines originating such works symmetrically do not have sufficient volition to infringe. If such a system persists, then copyright law may not play a major role in generative AI, though this is doubtful. Second, such inconsistencies inevitably result from causation analyses for mechanically analogous actions that only analyze one of infringement or authorship. Instead, I propose that copyright law symmetrically analyze mechanically analogous causation for both authorship and infringement of the reproduction right. Since copyright law has only recently begun analyzing causation, it is reasonable, and potentially desirable, that the law does not require this symmetrical causation. After all, the elements of authorship and infringement are usefully different. However, what has been consistent throughout copyright is that when an author creates, they risk both an infringing reproduction and the benefits of authorship rights. In other words, by painting, a painter may create a valuable copyrightable work, but they also may paint an infringing reproduction of Mickey Mouse. Asymmetrical causation for AI art could be analogized to the painter receiving authorship rights while the company that made the paintbrush being liable for the painter’s infringing reproductions. Such a result would not incentivize a painter to avoid infringement, and thereby improperly balance the risks and benefits of creation. Ultimately, if the law decides either the end-user or the AI company is the author, then the other entity should not be asymmetrically liable for infringing reproductions. Otherwise, the result will be ethically and logically inconsistent. After all, as Antony Honore wrote in Responsibility and Fault, in our outcome-based society and legal system, we receive potential benefit from and are responsible for the harms reasonably connected to our actions.

One thought on “Liability, Authorship, & Symmetrial Causation in AI-Generated Outputs

  1. Pingback: Revolutionizing Legal Practice with AI and Innovative Projects – AI Lawyer Talking Tech

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