Disclaimer: The views and opinions expressed in this article are expressed solely by Patrick McAndrew and do not necessarily reflect those of any organizations or affiliations he is associated with.

Responsible AI in entertainment means using AI tools with clear disclosure, consent, compensation, and human oversight.

It goes without saying that there is A LOT of hype around AI right now, especially in the world of entertainment. The Tribeca Festival premiered its first fully AI-generated film, Amazon MGM Studios has launched an AI studio and is testing AI tools in film and television production, and major labels, including Universal Music Group, Sony Music, and Warner Music Group, have signed licensing deals with AI music platforms. While studio executives and top entertainment companies are starting to bet big on AI with dollar signs in their eyes, artists are struggling to fight for themselves to ensure that their likeness, voice, and IP are not stolen to train the very systems these AI companies are developing.

The eagerness to leverage AI so as to reap its benefits, financial or otherwise, is a pattern we are seeing across several industries, but for an industry that relies heavily on the creative talents of humans, it is essential that AI isn’t just a fancy add-on to increase shareholder value. Rather, it must be used (if used at all) in a manner that protects artists, ensures human-in-the-loop processes, and has safeguards in place not only for the protection of humans, but also for our environment.

Artists are much less enthusiastic about using AI in their work, and for good reason. More so than the television, the personal computer, or the Internet, AI is being thrust upon us in a way that many detest. While there were certainly revolutionaries who pushed back on the tech of the past, it’s interesting to note that younger generations, who are often quick to adopt the hip, new tech, are pushing back the hardest on AI.

When someone’s creativity is at stake, let alone job displacement, it can feel like an attack on our humanity. As creative organizations increasingly adopt AI into their policies and procedures, leaders need to be aware of the importance of responsible AI implementation. Otherwise, the pushback will become so deafening that it will stop many studios in their tracks.

Studios and other entertainment leaders have an opportunity to distinguish themselves in an industry that thrives off human creativity. AI is already bringing about incredible tools that are revolutionizing the industry, but the adoption of said tools must be done methodically with responsible principles in mind. Otherwise, companies can face serious reputational backlash, (see Tilly Norwood as Exhibit A). While larger studios may see this as a pesky, little nuisance, smaller studios rely on their reputation to build an audience of loyal followers who will support their projects.

The list goes on and on for how many safeguards need to be put in place to ensure that all the boxes are being checked for responsible AI deployment, but for the purposes of today’s conversation, we will focus on three key AI literacy areas that every studio executive should be considering: disclosure and transparency, consent and training data, and compensation frameworks.

The hardest problems facing studios are no longer technical. The tools work and are getting better and better each day. The harder questions are about how these tools are used, and whether the people affected by that use were told, asked, and paid. Each of the areas we will explore has already produced cautionary tales, and each is something studio teams need to understand before a project begins.

The first question is the simplest to ask, but also the easiest to get wrong: does everyone who should know that AI was used actually know? Audiences, collaborators, and platforms increasingly expect to be told. YouTube, for example, has updated its monetization policies to require “genuine human creative direction” and to compel creators to apply disclosure labels for synthetic or altered content, treating transparency as a condition of doing business. Some creators have turned disclosure into a point of pride: the credits of Pluribus, the Apple TV series from Breaking Bad creator Vince Gilligan, declare that “This show was made by humans.”

The cost of non-disclosure is just as instructive. In April 2026, an AI-generated music-project, IngaRose, produced entirely with the generative AI platform Suno and with no corresponding real-life performer, briefly topped the U.S. and global iTunes sales charts before its synthetic origins became widely known. The reputational whiplash that follows these revelations is the real risk for studios. Dedicated AI festivals have already responded by writing transparency into their rules. The AI for Good Film Festival, for instance, requires filmmakers to maintain transparency in AI applications throughout the creative process. For an executive, the lesson is that disclosure is the foundation of audience trust, and once trust is lost to a feeling of having been deceived, no amount of production quality buys it back.

The second question is where the legal and ethical stakes are highest: where did the model's training data come from, and did the people who created that data agree to it? This is the heart of the most damaging controversy the industry has seen so far. To harken back to our early discussion, when the studio Xicoia promoted its AI "actress" Tilly Norwood for representation in late 2025, the actors' union SAG-AFTRA responded that she "is not an actor" but "a character generated by a computer program that was trained on the work of countless professional performers — without permission or compensation." The backlash was immediate and overwhelming, and it was driven less by the existence of the technology than by the absence of consent at its foundation.

Music has produced the clearest legal warning shots. In March 2026, a group of independent musicians sued Google, alleging that its Lyria 3 generative music model was trained on 44 million copyrighted clips and 280,000 hours of music scraped from YouTube without authorization. The exposure here is not limited to the companies that build the models. A studio that adopts a tool trained on unlicensed work inherits both the legal risk and the reputational damage, even though it did none of the scraping itself. Teaching teams to interrogate the provenance of a tool, to ask vendors what a model was trained on, and to respect existing frameworks around likeness rights and digital twins is no longer optional. Doing this will make the difference between a production company with safeguards in place and one that has a lawsuit waiting to be filed against them.

The third question assumes the first two have been answered well: even with disclosure and consent in place, who actually gets paid? This is where good intentions meet hard economics, and where the industry is still visibly struggling. As mentioned previously, Universal Music Group, Sony Music, and Warner Music Group had each entered licensing agreements with generative AI music platforms, establishing the first commercial frameworks for how AI-generated audio intersects with royalty flows and artist compensation. With that said, none of those frameworks have been well received by the artist community, a reminder that a deal existing is not the same as a deal being fair.

For studios, compensation is also a matter of contractual survival. SAG-AFTRA has made clear that signatory producers may not use synthetic performers without complying with obligations that require notice and bargaining whenever such a performer is used. The commercial pull toward AI talent is real. Hallwood Media's reported $3 million record deal with the AI music artist Xania Monet shows how quickly synthetic performers can generate revenue. But the equally aggressive pull via backlash shows the cost of building that revenue on a foundation that many humans see as extractive. The executives who navigate this well will be the ones who treat compensation as the mechanism that keeps the creative ecosystem, and their own access to it, intact.

Answering these three questions well cannot and should not rest on the instincts of individual producers and studio executives. Rather, it requires structure. In practice, responsible AI governance inside a studio means a clear, written policy on when and how generative AI tools may be used, a defined approval path for projects that involve them, and a cross-functional review body which includes legal, creative, technology, and labor relations at the same table. This committee must be empowered to vet new tools for training-data provenance and to sign off before AI work enters a pipeline. Studios that bake this knowledge in early will move faster and more confidently than those forced to learn it in the middle of a crisis.

Artists must also continue voicing their concerns, their hesitations, and their rights. AI has the potential to threaten all aspects of the creative process, both at an individual level and at the organizational level. We must hold powerful studios accountable for their actions, elevate those who are implementing responsible AI practices, and educate those who are using AI as a fast-track for profit. Especially the longer this technology is around and as the hype dies down, the studios who create exceptional AI governance mechanisms from the start will come out on top with a solid reputation in place, a community of artists behind them, and the ability to leverage this technology in a trustworthy, and therefore effective, way.

Q: Why is AI adoption in entertainment creating so much tension?
A: AI is creating tension because film, television, and music rely heavily on human creativity, performance, voice, likeness, and intellectual property. When AI tools are used without clear consent, disclosure, or compensation, artists may see the technology as a threat to their work and identity rather than as a creative tool.

Q: What should entertainment executives ask before using AI?
A: Executives should be able to answer three basic questions before using AI in a project: was AI use clearly disclosed, did the people whose work or likeness may be involved give consent, and is there a fair compensation structure in place?

Q: Why does disclosure matter for AI-generated entertainment?
A: Disclosure matters because audiences, collaborators, platforms, and artists increasingly expect transparency when synthetic or altered content is used. If viewers or artists feel misled, the reputational damage can outweigh any technical or creative benefit.

Q: Why is training data such a major issue?
A: Training data is a major issue because many AI systems are built using large collections of creative work. If that material was used without permission, studios that rely on those tools may face legal, ethical, and reputational risk, even if they did not build the model themselves.

Q: How could AI affect artist compensation?
A: AI could change how performers, musicians, writers, and other creators are paid when their work, style, voice, or likeness contributes to synthetic content. Licensing deals, royalty structures, and union protections may become central to whether AI adoption is accepted or rejected by the creative community.

Q: What does responsible AI governance look like for a studio?
A: Responsible AI governance means having a written policy for AI use, a clear approval process, and a review group that includes legal, creative, technology, and labor perspectives. Studios need a way to evaluate tools, check training-data provenance, protect artists, and approve AI use before it enters production.

About The Author:

Patrick is a responsible AI strategist, writer, and actor based in New York City. He is the Founder of the Future of Entertainment Alliance, a new initiative that advocates for human creativity in entertainment as emerging technologies like AI continue to revolutionize the industry. Patrick’s work focuses on the benefits of implementing responsible AI practices with expertise in entertainment and media. He currently works on the responsible AI team at HCLTech and has worked for the Responsible AI Institute and the Entertainment Community Fund.