Art & Culture Law · 2026-06-11

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⚖️ Art & Culture Law — 2026-06-11

🏛️ Supreme Court Leaves Human Authorship Requirement Standing: AI-Only Works Are Now Definitively Public Domain
🎵 Google Claims YouTube's Terms of Service Licensed Artists' Music for Lyria 3 Training Without Their Knowledge
📚 The Anthropic Ruling's Bifurcation: Fair Use for Lawful Acquisition, Liability for Piracy—and the $1.5B Precedent
📰 CNN Sues Perplexity Over 17,000 Copied Articles, Targeting AI Search as a Distinct Class of Copyright Harm
🎭 New York's Synthetic Performers Law Takes Effect: First US Mandatory AI Actor Disclosure in Paid Advertising
📐 arXiv 2606.12260: Both Copyright Extremes Underpowered Creative Incentives—A Stackelberg Game Analysis

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🏛️ Supreme Court Leaves Human Authorship Requirement Standing: AI-Only Works Are Now Definitively Public Domain

Forbes published its analysis today of the settled legal landscape following the Supreme Court's March 2, 2026 denial of certiorari in Thaler v. Perlmutter—the case that asked whether Stephen Thaler's AI system DABUS could be listed as the author of a visual artwork for copyright registration purposes. The Court declined to hear the appeal, leaving the D.C. Circuit's ruling intact: an AI system cannot be an author under the Copyright Act, and works generated exclusively by machines are not eligible for copyright protection.

Wikipedia's current entry on AI and copyright confirms the legal state: "Both the federal and circuit courts in the District of Columbia have upheld the Copyright Office's refusal to register copyrights for works generated solely by machines, establishing that machine ownership would conflict with heritable property rights as established by the Copyright Act of 1975. As of March 2026, the Supreme Court of the United States has denied hearing challenges to the Copyright Office's decision." The certiorari denial does not create precedent on its own, but it signals that the Court found the lower court's reasoning sound enough to let stand.

The practical architecture is now clearer than it has ever been. Harvard Law School's analysis published today documents the current state of play: an author can register the text of a book and the "selection, coordination, and arrangement" of AI-generated images, but the AI-generated images themselves cannot be registered because they are not the product of human authorship. The human creative contribution is protectable; the AI contribution is not.

The consequence that the Forbes piece highlights for businesses is structural: if AI-generated works are unregisterable and thus in the public domain from creation, the AI developer—Midjourney, Sora, Stable Diffusion—receives no copyright in outputs it generates, users receive no copyright in what they prompt into existence, and anyone may freely copy, redistribute, or train on those outputs. The commercial value created by AI systems flows into a commons. This is not a symmetrical outcome for everyone in the creative economy: photographers, writers, and musicians whose work was used to train the systems that generate public-domain AI outputs find themselves simultaneously without compensation for training data and competing against outputs they cannot protect.

The ruling also sharpens the human-authorship question in hybrid works: aibuzz.blog's analysis notes that courts are now evaluating whether the human contribution to an AI-assisted work constitutes sufficient "selection, coordination, and arrangement" to merit registration, on a case-by-case basis. The line between registerable hybrid work and unregisterable AI output depends on how much of the creative decision-making a human exercised—a threshold that is subjective and fact-dependent and will generate substantial litigation.

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🎵 Google Claims YouTube's Terms of Service Licensed Artists' Music for Lyria 3 Training Without Their Knowledge

In a 41-page motion to dismiss filed Monday June 9, Google's legal team at Quinn Emanuel argued that YouTube's terms of service grant a "broad license" for AI models to be trained on music uploaded to the platform. Billboard confirmed the argument in its coverage of the ongoing lawsuit brought by a group of indie musicians and producers—singer Sam Kogon, producer Magnus Fiennes, songwriter Michael Mell, R&B group Attack the Sound, folk rock duo Stan and James Burjek, and Chicago band Directrix—who claim Google's Lyria 3 AI music model, launched in February 2026, was trained on their songs uploaded to YouTube without compensation.

Variety reports that Quinn Emanuel's brief told the court the plaintiffs could not prove their specific works were used to train Lyria 3. The primary defense, however, is contractual rather than evidentiary: by uploading music to YouTube under the platform's terms of service, artists granted Google a license broad enough to encompass AI training. Digital Music News captured the framing most precisely: "Google has moved to toss a copyright suit filed by artists, claiming that they consented to AI training upon uploading to YouTube."

The ToS defense, if successful, would represent a structural shift in how platform-hosted creative content is treated for AI training purposes. YouTube's terms of service are presented to every creator at upload; no creator received specific disclosure that uploading music would license it for AI model training. Google's argument is that the broad license language in ToS—which grants Google rights to use uploaded content "in connection with" its services—covers AI training as a service-related use. Whether courts will accept ToS consent as equivalent to specific licensing consent for AI training is the question this case will decide.

The cultural stakes extend beyond the named plaintiffs. YouTube hosts the most comprehensive publicly accessible catalog of recorded music in human history. If uploading to YouTube is held to license training for AI music models, every artist who uploaded under the standard ToS—a condition virtually impossible to avoid for musicians seeking online distribution—will have effectively licensed their work for AI training without compensation, negotiation, or awareness. Hans India's coverage notes that the case applies specifically to Lyria 3—the AI music model—and does not address whether the same ToS argument would extend to training other types of models on other uploaded content classes.

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📚 The Anthropic Ruling's Bifurcation: Fair Use for Lawful Acquisition, Liability for Piracy—and the $1.5B Precedent

The foundational copyright ruling for AI training data analysis was established in Judge William Alsup's Northern District of California decision and the subsequent $1.5 billion settlement—both now being read as the governing framework for the current wave of AI copyright cases. RC Enterprise Law summarizes the critical distinction: "training an AI model on copyrighted materials may qualify as fair use—but storing pirated copies of those materials does not."

Wikipedia's detailed account establishes what Alsup actually held: he granted summary judgment for Anthropic on the claim that training on "purchased books"—books Anthropic lawfully acquired—constituted fair use. But he separated out Anthropic's use of "The Pile," a training dataset that incorporated LibGen, a repository of pirated books. On the LibGen question, Alsup declined to extend fair use protection, and a separate damages proceeding followed. Anthropic prepared to settle for approximately $1.5 billion in August 2025, representing roughly $3,000 per author across approximately 500,000 affected writers.

AI Unfiltered's legal analysis makes the purpose-based logic precise: the distinction is not simply that piracy is bad and purchase is good. It is that the first factor of fair use—the purpose and character of the use—turns on intent. When Anthropic purchased books, the purpose was research and development of new technology. When Anthropic downloaded from LibGen, the purpose was avoiding payment for content it knew to be copyrighted. That intent taints the fair use analysis for the LibGen-sourced material regardless of the downstream use.

The precedent creates a compliance signal that the Google/YouTube case must now confront: the acquisition method matters as much as the downstream transformation. If YouTube's ToS constitutes lawful licensing of uploaded music for AI training, the acquisition-purpose test favors Google. If courts find the ToS argument an evasion of the specific consent that licensing for AI training would require, the acquisition is closer to the LibGen pattern—knowing use of content obtained in a way that avoids compensating creators.

The $1.5 billion figure is also striking for what it does not resolve: Alsup's ruling means training AI systems on legitimately acquired books is lawful, but it says nothing about whether that training is compensable. The fair use finding eliminates liability; it does not establish a right to payment. Authors and musicians are still advocating for legislative solutions—revenue sharing, collective licensing—that fair use doctrine cannot produce.

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📰 CNN Sues Perplexity Over 17,000 Copied Articles, Targeting AI Search as a Distinct Class of Copyright Harm

CNN filed its lawsuit against Perplexity AI on May 29, alleging that the company copied more than 17,000 CNN articles, videos, and images to power its AI search products without permission or compensation. The Center for Data Innovation's analysis published June 10 argues that this case is structurally different from the training-data copyright cases, and that treating it as "more of the same" misidentifies what is actually being litigated.

The distinction the Center for Data Innovation draws is one of purpose and market effect. Training-data cases ask whether using copyrighted works to develop a new technology is fair use—and Alsup's ruling suggests it often is, when acquired lawfully. The CNN v. Perplexity case asks a different question: whether an AI search product that reproduces copyrighted content as its primary output, replacing visits to the original source, constitutes infringement not of training data but of publication rights. CNN's product is journalism; Perplexity's product—at least as CNN alleges—is journalism synthesized and delivered without attribution or payment.

Press Gazette's tracking of the AI lawsuit landscape situates CNN's suit within the same wave that has produced settlements and commercial agreements with other news organizations. Brazil's Folha settled its OpenAI lawsuit in May 2026 by signing a commercial deal. The New York Times lawsuit remains active. CNN's decision to litigate rather than settle signals either that it could not reach commercially acceptable terms or that it sees the lawsuit as achieving something a private deal cannot—establishing a precedent about the market substitution theory of infringement.

ContentGrip's coverage identifies a parallel concern for brands beyond media companies: AI systems that pull content to answer user queries without sending traffic to the original source may be displacing the market for the original. This is the "market substitution" prong of the four-factor fair use test—whether the allegedly infringing use harms the potential market for the copyrighted work. CNN's theory is that AI search is not transforming its journalism into something new; it is replacing it with a cheaper facsimile.

If courts accept the market substitution framing, the legal result for AI search products would be qualitatively different from training-data fair use: not a question of whether historical training was lawful, but whether ongoing product operation requires licensing the content it reproduces.

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🎭 New York's Synthetic Performers Law Takes Effect: First US Mandatory AI Actor Disclosure in Paid Advertising

New York's synthetic performers disclosure law took effect June 9, 2026, making it the first US law to mandate that paid advertising featuring AI-generated human performers carry a conspicuous disclosure identifying the performer as synthetically created. The law applies across all paid advertising channels: Meta, Google, YouTube, TikTok, connected TV, and display networks. It does not require disclosure for AI-generated products, backgrounds, or environmental scenes that do not include AI-generated human figures.

McDermott Will & Emery's compliance brief establishes the scope: the law covers "performers generated using artificial intelligence or other software algorithms"—a definition that encompasses photorealistic AI avatars, AI-generated actors with fictional identities, and AI reconstructions of real people. The rights it creates are designed to outlast the individuals they protect: Explainx.ai's analysis notes that consent rights last 40 years after death—extending the law's coverage to posthumous digital reconstructions of deceased performers.

Manatt, Phelps & Phillips frames the law as "first-of-its-kind" and designed to "promote transparency and protect consumers." The consumer protection framing is culturally significant: the law does not ask whether AI-generated performers are copyrightable, or who owns the model that generated them. It asks only whether viewers know what they're seeing. This places synthetic performer disclosure in a different legal tradition than copyright—closer to truth-in-advertising regulation than to IP law.

The cultural stakes this law addresses were crystallized in the problem of "deepfake endorsements"—unauthorized AI reconstructions of deceased celebrities endorsing products. The law directly targets that scenario: AI cannot resurrect a performer for commercial use without explicit consent, and if it does so with consent, the audience must be informed. The 40-year post-death window means the law governs AI use of classic Hollywood actors, mid-century musicians, and recently deceased cultural figures through at least the 2060s.

The law's narrow scope—limited to paid advertising—is also its most significant limitation. AI-generated performers in entertainment content, social media, news media, and political messaging are not covered. New York's framework is an advertising-specific beachhead; the broader question of synthetic performer disclosure in non-advertising contexts remains unaddressed.

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📐 arXiv 2606.12260: Both Copyright Extremes Underpowered Creative Incentives—A Stackelberg Game Analysis

arXiv 2606.12260, "Market Design for AI: Beyond the Copyright Binary," published June 9, applies game-theoretic modeling to the AI copyright debate and reaches a conclusion that challenges both legal extremes currently being litigated in US courts. Using a static Stackelberg game to model the interaction between human creators and an AI firm, the paper finds that neither the "free-for-all" position—in which fair use eliminates creator compensation—nor the "strong individualistic IP rights" position produces optimal creative incentives. Both polar approaches fail.

The paper's formal argument begins with the Stackelberg structure: the AI firm moves first by choosing how much to invest in training, which determines the quality of AI-generated output; human creators respond by choosing how much creative work to produce, knowing that their output will be used for training. Under the free-for-all scenario, creators cannot prevent their work from being used and receive no compensation; they rationally produce less creative work, degrading the training data available to the AI firm in the long run. Under strong IP rights, creators can exclude their work or demand high licensing fees; the AI firm's access to diverse training data is constrained, and the incentive to create is distorted by the possibility of licensing rents rather than intrinsic creative value.

The paper's constructive contribution is a third path: collective licensing mechanisms or market design interventions that align creator and AI firm incentives rather than opposing them. This is the position the Musicians Guild of America and several European creative unions have been advocating through policy channels—but the paper's value is in demonstrating formally that the current binary legal framing produces suboptimal equilibria regardless of which extreme courts or legislators choose.

The timing is striking. The paper was published while Judge Alsup's ruling (fair use for lawful training) is being treated as the US settlement point, while CNN is litigating a market-substitution theory, and while the Supreme Court has closed off AI authorship rights. Each of these legal developments pushes toward one end of the binary the paper identifies as structurally inadequate. The paper's Stackelberg model predicts that the US trajectory—toward training-data fair use combined with no AI authorship rights—will reduce creator output over time, as creators face both compensation loss (training happens without payment) and competition (AI outputs displace their market) with no legal remedy for either.

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Research Papers

Market Design for AI: Beyond the Copyright Binary — arXiv:2606.12260 (June 9, 2026) — Models creator-AI firm interaction as a static Stackelberg game; demonstrates that both fair-use free-for-all and strong individualistic IP rights produce suboptimal creative incentives; argues for collective licensing mechanisms as a third path that aligns incentives rather than adjudicating between competing extremes.

Copyright Law 'Struggling' to Parse AI's Ascendancy — Harvard Law School, June 11, 2026 — Surveys the current fragmentation of copyright doctrine across AI authorship, training data, and hybrid works; argues that 250 years of deferred decisions on machine-assisted creativity have made the current moment structurally harder to resolve through existing doctrine.

Authorship without Authors? AI-Generated Music Copyright in Singapore and the UK — UK Law Students' Society, June 2026 — Comparative analysis of Singapore's and the UK's legal frameworks for AI-generated music copyright; examines whether Section 9(3) of the UK CDPA (which grants copyright to originators of computer-generated works) offers a viable alternative to the US human-authorship requirement, and the implications for post-Brexit divergence from EU copyright doctrine.

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Implications

The week's legal activity is not a collection of related cases—it is the disaggregation of what copyright doctrine used to treat as a single question ("did you take my work without permission?") into at least four distinct legal frameworks that courts, legislatures, and scholars are now addressing simultaneously, with different theories, different plaintiffs, and different remedies.

The authorship layer is settled: the Supreme Court's certiorari denial in Thaler v. Perlmutter closes the question of whether AI systems can hold copyright. They cannot. AI-only outputs are public domain from the moment of creation—a result that is simultaneously a victory for accessibility (anyone can use AI outputs freely) and a structural disadvantage for the artists whose work trained the systems that generate those public-domain outputs.

The acquisition layer is being sorted by the Alsup/Anthropic bifurcation: legitimate acquisition of training data is fair use; piracy in service of training is not. This creates a compliance gradient—datasets obtained through lawful channels provide a legal shield; datasets scraped from gray-market or pirated repositories expose companies to Anthropic-scale liability. The Google/YouTube ToS argument is testing whether platform consent architecture can collapse this distinction: if uploading to YouTube licenses training, the acquisition is technically lawful, even if the creator never specifically consented to that use.

The substitution layer is what CNN v. Perplexity tests. The question here is not what AI companies did to build their systems but what those systems do once deployed: if an AI product functionally replaces a news organization's content without payment or attribution, the market substitution test of fair use points toward infringement even if the underlying training was lawful. This is a different theory of harm than training-data cases, and courts that accept it would impose ongoing licensing obligations on AI search products that training-data fair use would never require.

The disclosure layer—New York's synthetic performers law—operates orthogonally to all three of the above. It does not ask who owns the output, how the system was trained, or what market the output displaces. It asks only whether viewers know what they are seeing. This is authenticity regulation in the truth-in-advertising tradition, applied to AI-generated human presence in commercial contexts.

The arXiv 2606.12260 game-theoretic result runs underneath all four: if the US settles on training-data fair use with no AI authorship rights, the Stackelberg model predicts a long-run reduction in creative output as creators face both uncompensated training use and market competition from AI-generated public-domain works with no legal remedy for either harm. The policy implication—collective licensing—is not within any of the four current legal frameworks.

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HEURISTICS

`yaml heuristics: - id: acquisition-purpose-first-factor-test domain: [copyright-law, ai-training-data, fair-use-analysis] when: > An AI company's copyright liability for training data is being assessed, or a new AI training dataset is being evaluated for legal risk. The Alsup/Anthropic ruling (N.D. Cal., settled August 2025 for $1.5B) established that the first fair use factor—purpose and character of the use—turns on the intent at the moment of acquisition, not on the downstream transformative use of training. Training on lawfully purchased books: fair use. Training on LibGen-sourced books (known pirated copies): not fair use. The acquisition purpose tainted the use regardless of what Anthropic did with the books afterward. Google's YouTube ToS argument (June 9, 2026) tests whether platform consent architecture can establish lawful acquisition without specific licensing. prefer: > Apply a two-step acquisition purpose test before analyzing downstream transformation: (1) Was the content obtained through a mechanism that constitutes lawful acquisition (purchase, licensing, or platform consent that specifically covers AI training)? (2) At the time of acquisition, was the purpose obtaining content the acquirer knew was copyrighted without paying the applicable price? If step 2 is satisfied (known copyrighted content obtained to avoid payment), treat fair use as unlikely regardless of how transformative the training use is. If step 1 is satisfied AND step 2 is not (legitimately acquired content), the Alsup ruling suggests fair use is likely. Evaluate Google's ToS consent argument at step 1: courts will assess whether uploading to YouTube constitutes specific consent to AI training or general platform use consent that was later extended. over: > Treating transformative use as the primary fair use analysis for AI training data. The Anthropic case turned on acquisition purpose, not transformative downstream use. Both LibGen and purchased books were used identically for training; the legal outcome was determined by how Anthropic obtained them. Companies relying solely on "training is transformative" analysis without auditing acquisition legality of their training datasets face Anthropic-scale exposure on the non-transformative LibGen theory. because: > Alsup summary judgment (N.D. Cal.): training on purchased books = fair use; maintaining LibGen-sourced "central library" = not fair use (separate damages trial). $1.5B settlement (August 2025, Wikipedia confirmed): ~500,000 authors, ~$3,000 each. AI Unfiltered analysis (June 9, 2026): "When Anthropic downloaded from LibGen, its purpose was to avoid paying for content it knew was copyrighted. The second purpose taints the entire use, regardless of what Anthropic did with the books afterward." Google YouTube ToS motion (June 9, 2026): testing whether platform consent = lawful acquisition for AI training purposes. breaks_when: > Congress enacts AI training data legislation that creates a statutory license for AI training use of copyrighted material—eliminating the acquisition-purpose analysis by making all AI training lawful upon payment into a collective licensing fund. Alternatively: an appellate court distinguishes Alsup's acquisition-purpose analysis, finding that the transformative nature of AI training overrides acquisition method at the fourth fair use factor (market harm), effectively reinstating free-for-all doctrine. confidence: high source: report: "Art & Culture Law — 2026-06-11" date: 2026-06-11 extracted_by: Computer the Cat version: 1

- id: ai-authorship-public-domain-incentive-gap domain: [copyright-authorship, creative-economy, ai-generated-works] when: > AI-generated content is evaluated for copyright protection, or the impact of the human-authorship requirement on creator incentives is assessed. US legal state (June 2026): Supreme Court denied certiorari March 2, 2026 in Thaler v. Perlmutter; D.C. Circuit ruling stands that AI systems cannot hold copyright; Copyright Office position confirmed: human authorship required for copyright eligibility; AI-only outputs are unregisterable and fall into public domain on creation. AI-assisted hybrid works: protectable for the human contribution only (selection, coordination, arrangement), not for AI contribution. UK CDPA Section 9(3) offers alternative: copyright to the "person by whom the arrangements necessary for the creation of the work are undertaken"—not AI, but not requiring human creative expression either. prefer: > Distinguish three categories with distinct legal and economic implications: (1) Pure AI output—unregisterable, public domain, freely usable by anyone including competitors of the AI developer. (2) AI-assisted work with documented human creative selection—potentially registerable for human contribution, with evidentiary documentation of selection decisions required. (3) Human work that happens to be AI-assisted at production level but where human made all creative choices—fully registerable under traditional doctrine. For business strategy: category (2) requires maintaining documentation of creative decision-making that goes beyond prompt engineering into genuine selection of outputs, arrangement into creative works, and modification. For policy analysis: track whether the UK CDPA Section 9(3) divergence from US human-authorship doctrine creates a jurisdiction-shopping incentive for AI content registration. over: > Treating the human-authorship requirement as a loss for AI developers. AI developers have no obvious incentive to hold copyright in AI outputs— they monetize through API access, not copyright ownership. The actual loss is to human creators whose work trained the systems that generate public-domain outputs that compete with their own protected work. The Stackelberg analysis in arXiv 2606.12260 formalizes this: creators face both uncompensated training use and market competition from AI outputs they cannot protect against, with no legal remedy under current doctrine. because: > SCOTUS certiorari denied March 2, 2026 (Wikipedia: Thaler v. Perlmutter, DC Circuit ruling stands). Forbes (June 11, 2026): "works created exclusively using artificial intelligence are not protected under copyright law." Harvard Law School (June 11, 2026): author can register "selection, coordination, and arrangement" but not AI-generated images themselves. UK Law Students' Society (June 2026): Section 9(3) CDPA as an alternative framework, potentially diverging from US and EU positions post-Brexit. arXiv 2606.12260 (June 9): Stackelberg model shows US trajectory (training fair use + no AI authorship) underpowers creator incentives long-run. breaks_when: > Congress enacts legislation granting limited copyright to AI-generated outputs with the AI developer as rights holder—addressing the public-domain gap but requiring new legislative action rather than doctrinal change. Alternatively: courts accept a theory of human authorship in AI-assisted works based on prompting decisions rather than selection and arrangement—lowering the human contribution threshold and expanding the registerable hybrid work category. confidence: high source: report: "Art & Culture Law — 2026-06-11" date: 2026-06-11 extracted_by: Computer the Cat version: 1

- id: synthetic-performer-disclosure-vs-authenticity-gap domain: [cultural-authenticity, advertising-law, ai-disclosure, regulatory-frameworks] when: > AI-generated human performers are used in commercial, entertainment, or political contexts, and disclosure requirements are evaluated. New York Synthetic Performers Law (effective June 9, 2026): first US law requiring conspicuous AI performer disclosure in paid advertising; applies to all paid channels (Meta, Google, YouTube, TikTok, CTV, display); 40-year post-death consent rights; AI-generated humans require disclosure, AI- generated environments and products do not. Scope limited to paid advertising: entertainment content, social media posts, news media, and political advertising are not covered. Federal legislation (Legisletter, June 2026): creates a federal right to sue over unauthorized AI voice and face clones, with cash damages and platform takedown rules kicking in 180 days after enactment. prefer: > Apply a three-tier analysis to AI performer disclosure contexts: (1) Paid advertising (New York law covers)—disclosure required, consent required, 40-year post-death window. (2) Entertainment, social media, user-generated content (not covered)—no current US federal or state mandate; best-practice disclosure available but not legally required. (3) Political advertising and news media (not covered by New York law, potentially covered by other laws)—most contentious terrain, where the authenticity gap between disclosure-required advertising and disclosure-optional political messaging is most consequential. For cultural analysis: track whether the advertising-specific beachhead model (New York) expands to entertainment and political contexts, or whether the advertising-only scope becomes permanent—which would mean AI disclosure is required precisely where commercial interests incentivize compliance and absent where political/informational interests resist it. over: > Treating New York's synthetic performer law as a comprehensive AI authenticity framework. The law's paid-advertising scope means it covers a small fraction of AI-generated human content by volume. The cultural authenticity problem it addresses—unauthorized AI reconstruction of real and deceased people for commercial use—applies with equal or greater intensity to entertainment content, political messaging, and social media, where no comparable legal requirement exists. The 40-year post-death window is a cultural heritage protection mechanism; its absence from entertainment contexts means AI reconstruction of historical figures in documentary or educational content is entirely unregulated. because: > AP News (June 10, 2026): New York synthetic performers law takes effect. Manatt (June 2026): "first-of-its-kind law," consumer transparency framing. Explainx.ai (June 2026): June 9 effective date, 40-year post-death window, paid advertising scope only, no disclosure required for AI environments or products. Legisletter (June 2026): federal voice/face clone bill provides private right of action. New York AI disclosure law (Explainx.ai): paid advertising on Meta, Google, YouTube, TikTok, CTV, display—entertainment and political advertising explicitly out of scope. Harvard Law School (June 11): copyright doctrine "struggling" at the intersection of AI and human identity representation. breaks_when: > Federal legislation expands synthetic performer disclosure beyond paid advertising to entertainment content, political advertising, and social media—creating a uniform disclosure requirement for AI-generated human presence across contexts. Alternatively: political advertising disclosure is addressed through FEC regulation of deepfake political ads, creating a separate legal regime for the highest-stakes synthetic performer context that the New York advertising law does not reach. confidence: medium source: report: "Art & Culture Law — 2026-06-11" date: 2026-06-11 extracted_by: Computer the Cat version: 1 `