AI is changing how news and entertainment are researched, produced, edited, distributed and monetized. It can speed up tasks such as transcription, translation, visual-effects work and content search, but it also raises questions about accuracy, consent, copyright, jobs and audience trust. The key distinction is whether AI is assisting a human decision-maker or generating material that is presented as someone’s work, likeness or reporting without meaningful review or permission.
Where AI is changing media work
Generative AI produces text, images, audio or video in response to inputs; other AI systems classify, recommend, transcribe or analyze material. In news and entertainment, these systems can support both routine production and creative or editorial decisions. The International Labour Organization’s 27 February 2025 brief describes changes to tasks in journalism, music and film production, while noting implications for skills, oversight and working conditions.
The practical question is not simply whether a team uses AI. It is what the system does, what source material it uses, who checks the result, and who remains responsible for the final work.
| Area | Examples of AI assistance | Questions the team should resolve |
|---|---|---|
| Newsrooms | Transcription, translation, summarization, metadata, personalization and stories built from structured data | Are facts and framing reviewed by a human? Are sources protected? Is synthetic alteration disclosed where it could mislead? |
| Film and television | Ideation, script or dialogue support, previs, storyboarding, visual effects, dubbing, localization and restoration | Who approves creative choices? Are likenesses and voices authorized? How are affected workers credited and compensated? |
| Music and other entertainment | Music generation, recommendation, marketing variations and search across large catalogs | Were training materials licensed? Are style, performance or voice being imitated? How is value shared with creators? |
How AI is changing journalism
Routine tasks and editorial judgment
AI can help handle high-volume work such as converting speech to text, translating material, summarizing documents and adding searchable metadata. It can also generate or personalize content. These functions may save time, but a fluent output is not proof that a claim is accurate or that a summary preserves context. Human editors still need to check original records, quotations, numbers and framing before publication.
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A defensible newsroom workflow identifies where AI was used, assigns a person to approve consequential output, checks source material and protects confidential information. Disclosure matters when synthetic material or substantial AI alteration could change what audiences think they are seeing or hearing. There is no single disclosure rule established for every outlet and jurisdiction; policies differ, so a newsroom should explain its own approach rather than imply a universal standard.
Audience use and trust
The Generative AI and News Report 2025 reports that the share of respondents who said they had used generative AI to get the latest news rose from 3% in 2024 to 6% in 2025. The report attributes the increase mainly to changes in Japan and Argentina. That finding describes the report’s measure and should not be read as a universal rate for every country or as evidence that audiences trust AI-generated news.
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How AI is changing film, television and music
Assistance can widen creative options
Production teams can use AI to explore visual ideas, develop previs or storyboards, search material, create language versions, support restoration or iterate on marketing assets. Used as an aid, it can shorten parts of a workflow while leaving a human creative decision-maker responsible for selection, revision and approval. The ILO’s sector analysis includes film production and music, and the World Economic Forum considers news media, publishing, broadcasting, entertainment and sport within a shared AI-governance frame.
Substitution raises consent and credit questions
When synthetic output stands in for a performer, writer, musician or other creator, the issues are more than technical. Teams need to consider permission to use a person’s voice or likeness, how style and performance are represented, employment and residuals, attribution, and whether audiences should be told that a performance is synthetic. A licensed digital double is different from a realistic depiction distributed without the person’s consent.
These questions apply across media, but the relevant contracts and laws can differ by jurisdiction and type of work. The available evidence does not establish that any particular studio, label or platform uses a specific AI tool, so tool adoption should be assessed from current, source-specific information.
Copyright, training data and digital replicas
U.S. copyright depends on human creative expression
For U.S. copyright guidance, the Copyright Office’s Part 2 report says an AI-assisted output may be protected when a human author determines sufficient expressive elements. That can include human-authored material perceptible in the result, as well as creative arrangement or modification. The Office says the mere provision of prompts is not enough by itself to establish copyright. The outcome is fact-specific: using AI does not automatically remove protection, but a prompt alone does not make a person the author of everything the system generates.
This is U.S. guidance, not a settled rule for every country. It also does not resolve every question about whether copyrighted works may be used to train models. The Copyright Office’s broader study considered digital replicas, copyrightability and training data as separate issues; licensing, liability and training-data legality remain active policy questions.
Unauthorized digital replicas are a distinct harm
The U.S. Copyright Office’s Part 1 report describes unauthorized digital replicas as a serious threat in entertainment, politics and private life and recommends federal legislation protecting people from the knowing distribution of such replicas. The recommendation is not itself a universal legal rule. For creators and producers, it underscores why consent records and clear limits on how a voice or likeness may be used matter, particularly when a synthetic depiction could be mistaken for a real performance or statement.
Best Value
Who benefits—and who bears the costs?
Job exposure is about tasks as well as job titles
AI can change how a task is done without eliminating an entire role. Transcription, translation, search or early-stage content generation may be affected differently from reporting, editing, direction or performance, where judgment, context and responsibility remain central. The ILO calls for policy frameworks, ethical AI governance, social dialogue, fair compensation and creative control for workers. The available evidence does not establish a reliable overall figure for media jobs eliminated, so a precise job-loss total should not be inferred.
Creator income and licensing are contested
CISAC, a rights-industry organization, estimates that generative-AI music services could reach €4 billion in revenue in 2028. Separately, its 2025 collections release estimates that unlicensed generative AI could divert up to 25% of creators’ royalties—equivalent to €8.5 billion annually—if left unregulated. These are projections and estimates from a rights-industry source, not settled outcomes or a measured loss already incurred.
The underlying economic question is how value is allocated when models are trained on or generate material connected to journalism, recordings, scripts, performances or images. Agreements may need to address permission, payment, attribution, opt-out mechanisms, data provenance and liability. The commercial opportunity therefore includes rights administration, consent and provenance systems alongside tools that generate content.
How audiences can assess synthetic or AI-assisted media
No single clue reliably proves that media is authentic or AI-generated. A polished image, voice or video can be synthetic, while genuine footage can look unusual because of compression, editing or recording conditions. Treat detection tools and visual tells as prompts for further checking, not as a verdict.
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- Find the original source. Look for the earliest available publication, the named person or organization responsible, and the context in which the item first appeared.
- Corroborate consequential claims. For a newsworthy statement, check whether credible independent reporting or an official record supports it. A repost or AI summary is not independent confirmation.
- Check context and provenance. Ask whether the clip is complete, when and where it was recorded, and whether a publisher identifies synthetic elements or material edits.
- Be cautious with supposed detection. An automated label or a visual oddity alone cannot establish who made the media, whether it was altered, or whether the depiction was authorized.
What a newsroom or production team should ask before adopting a tool
These checks turn the main editorial, rights and labor risks into procurement questions. A vendor’s answer should be specific enough to assess and should be reflected in the contract or workflow where it matters.
Quick Recap
- Accuracy and auditability: What can users inspect about sources, transformations and outputs? Where does a qualified human approve work?
- Training data and rights: What does the vendor disclose about training materials, licensing, attribution and opt-out controls? What indemnity applies, and what does it exclude?
- Consent and synthetic media: Can the team record and export voice or likeness permissions? Are synthetic outputs labeled, and can those labels be preserved through distribution?
- Data handling: Are confidential source material, scripts, recordings or unpublished work retained or used to improve the service? Can they be deleted?
- People and accessibility: How does the workflow affect staff responsibilities and compensation? Does it support the accessibility and localization needs of the audience?
- Continuity: Can project files and records be exported if the supplier changes its terms or the team stops using the service?
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