Specifically, the WGA has said “like all research material, [AI-generated material] has no role in guild-covered work, nor in the chain of title in the intellectual property. It is important to note that AI software does not create anything. It generates a regurgitation of what it’s fed. If it’s been fed both copyright-protected and public domain content, it cannot distinguish between the two. Its output is not eligible for copyright protection, nor can an AI software program sign a certificate of authorship. To the contrary, plagiarism is a feature of the AI process.”

It is thought that this distinction has been made intentionally by the WGA, to allow writers to benefit from AI software without having to worry about being pulled into credit arbitrations with that software’s manufacturers, who might otherwise claim that they are a legal author of a script that has been drafted by the software. However, the WGA has yet to opine publicly on a situation where an AI program writes a script entirely on its own, without prompts from a human.

AI models can also help streamline the editing process, reducing time and costs associated with relatively mundane framing, cutting and sound design tasks. Leading AI-driven tools already available can analyze video streams in real-time and detect key moments using various audio, visual and textual cues. For sports, these include points/goals, penalties, and other major moments, which has proven hugely successful.

Elsewhere, broadcasters are exploring potential uses for AI to facilitate the clearance process for third party intellectual property, as well as to help adhere with content regulations in difference countries. AI could relatively easily be harnessed to help create multiple versions of the same content that is specifically tailored for a range of languages, cultures and ages.

Large national broadcasters and studios also often benefit from content archives spanning back decades, which could be mined for educational purposes, including to inform (or even generate) the production of new content.

In this sense, AI is already being used across the film and TV industry to augment human creativity, rather than replace it entirely. However, these uses (and the many implications of them) also create unease.

Hollywood’s labor unions are watching closely, with SAG-AFTRA recently issuing a statement supporting the Human Artistry Campaign’s core principles for AI, reaffirming its position on digital voice, likeness and performance simulations.

SAG-AFTRA views the digital simulation of a performer as the creation of a new performance that is the subject of bargaining with the union. “In deeming a digital simulation a ‘new performance,’ SAG-AFTRA attempts to bring the use and reuse of such digital simulations under its collectively bargained contract provisions – namely, consent and negotiation of compensation, which are mandatory subjects of bargaining under the National Labor Relations Act.”

In taking this position, SAG-AFTRA has planted a flag in the AI space, making clear that while its current collective bargaining agreements may not specifically reference AI, it views the use of AI as a potential circumvention of a performer’s rights.

The same legal issues explored in detail in this guide also apply across the areas mentioned above. The ability of an AI model to break down a script is only as commercially viable as the data it has been trained on, and the legality of the use of that training set. Similarly, copyright law is very much playing catch-up in most countries when it comes to AI-driven tools creating new output based on existing content.

At this point, the use of AI in the film and TV industry cannot be stopped. Instead, the question for businesses, regulators and lawyers is: how effectively can it be controlled?