A New Framework for AI, Platforms & Creators: Fairness in the Age of Generative AI

Platforms leverage asymmetrical power.
Generative AI relies on vast troves of human-created content—often harvested without explicit consent. For platforms like Google, Meta, or AI developers, this content fuels innovation. For creators, it often goes uncredited and uncompensated. This imbalance echoes long-standing asymmetries that economists have studied in platform markets. Content creators drive the supply; platforms reap disproportionate

Copyright law is showing its age.
Current copyright mechanisms—based on reproducing and quoting specific works—don’t accommodate the diffuse, blended nature of AI-derived outputs. Counting how much a creator contributed to a generated image, summary, or composition is often impossible. This means proportional remuneration, a cornerstone of content rights, is difficult to apply.

Emerging frameworks offer promise.
Researchers and legal thinkers are responding with innovative proposals:

• Content ARCs (Authenticity, Rights, Compensation) propose decentralized, open standards for attribution and licensing. These systems allow creators to be identified, credited, and even paid when their work helps train AI.arXiv

• The CCC (Collective-Centered Creation) model treats AI-generated outputs as the product of a collaborative process—RIghts and credits should be distributed proportionally across contributors.SpringerLink

• Game-theory approaches could attribute compensation based on each source's influence on AI outputs—identifying creators whose work proved most decisive in generating new content.arXiv

Policy and licensing are catching up.
Governments and licensing bodies are starting to act. The UK is launching a collective license system for authors whose works are used in AI training, especially those who cannot negotiate individually. This model aims to standardize payment and simplify access.The Guardian

Meanwhile, the EU is questioning the default “opt-out” approach—where silence equals consent—and advocating instead for an opt-in model tied to transparency, traceability, watermarking, and compensation.PC Gamer

The courts are raising stakes—but creators still face challenges.
In the U.S., recent lawsuits have largely favored major AI developers. Courts have upheld that training on public content qualifies as “fair use,” reducing legal barriers to scraping content at scale.Business Insider

Tech firms and creators are reacting.
Companies like Adobe are already paying bonuses to contributors based on how much their works are used to train AI. Stock platforms like Canva have similar arrangements. Stability AI offers an opt-in revenue-sharing model with musicians.Quartz

But voluntary action can only go so far.
Ethical guidelines—such as those from #paid—stress that AI should help, not replace, human creativity; creators should retain control, remain transparent, and preserve authenticity.PR Newswire

Yet, if legal and economic incentives remain skewed, creators may withdraw from public webspaces entirely—as seen in moves such as “pay-per-crawl” platforms and creators migrating off major platforms.Business InsiderThe SunWikipedia

Why This Matters to TMFS Readers

1. Reinforces Credibility and Trust.
When creators aren’t fairly compensated, authenticity erodes. A modern framework would preserve creativity’s value and strengthen trust between AI makers, audiences, and original creators.

2. Aligns Innovation with Fair Play.
A fair market—built on licensing, transparent attribution, and shared value—levels the playing field. It turns AI from a drain on human creativity into a reinforcement of it.

3. Models Strategic Responsibility.
Forward-thinking platforms that adopt collective licensing, traceability, and contributor-led governance will shape a more sustainable creative economy.

Summary of Proposed Solutions

Solution TypeDescriptionContent ARCsDecentralized tech for attribution and licensing of AI training contentCollective LicensePooled compensation for creators where individual negotiation is impracticalOpt-In Legal FrameworkShift from opt-out consent to proactive consent tied to compensationEconomic Attribution FrameworksGame theory-based models to apportion revenue according to contributionVoluntary Bonuses & Revenue-SharingExamples: Adobe, Stability AI—encouraging but not comprehensive

Conclusion: A New Collective Bargain for the AI Era

We stand at a crossroads where technology can either cannibalize creativity or co-create its future. Without intervention, AI may erode the economic foundations that fuel human ingenuity. We need a new collective bargain: one where creators retain agency, AI development continues, and society has access to both innovation and authentic voices.

A modern framework—combining licensing markets, attribution systems, legal clarity, and equitable barter—is not a favor. It is a necessity. For platforms to thrive and for creativity to flourish, value must be fairly recognized and fairly shared.