The AI Copyright Transparency Act: Another Reactionary Law That Misses the Point
Legislation is meant to protect, inform, and serve the public. But what happens when lawmakers attempt to regulate technology they don’t fully understand? That’s exactly the issue with California’s AI Copyright Transparency Act (AB 412) — a bill that, while sounding good on the surface, ultimately fails to address the real dynamics of AI, data, and creativity.
At first glance, AB 412 seems like a win for artists, writers, and creators, giving them the right to know if their work has been used in AI training datasets. But dig deeper, and you’ll realize it’s based on misconceptions about how AI actually functions.
What the AI Copyright Transparency Act Seeks to Do
Introduced by Assemblymember Rebecca Bauer-Kahan (D-Orinda), AB 412 attempts to increase transparency regarding the use of copyrighted materials in training generative AI models. It includes provisions that:
✅ Require AI developers to disclose when copyrighted works have been used in training datasets.
✅ Create a public request system for creators to check if their work was included.
✅ Allow copyright holders to take legal action against AI developers who fail to disclose this information.
While these provisions may sound like a step toward fairer AI regulation, they overlook one crucial issue: AI does not work the way lawmakers think it does.
Misconceptions About How AI Works
The biggest flaw in this bill is that it assumes AI functions like a database, storing and regurgitating copyrighted materials verbatim.
In reality, AI learns patterns and structures, not exact content.
Misconception #1: AI “Steals” Content
There is a persistent belief that AI models “steal” creative works by simply copying them into a massive dataset. That’s not how AI functions.
AI does not store full copies of books, images, or articles.
It learns from patterns, much like how humans read, absorb knowledge, and then apply it in new ways.
Asking AI to generate an exact replica of The Great Gatsby will not return the book word-for-word — it will generate something influenced by similar storytelling structures.
If AI is “stealing,” then so is every human who reads, learns, and creates from existing knowledge.
Misconception #2: Creators Can “Protect” Their Work Once It’s Online
The moment something is publicly available online, it becomes part of the internet’s collective knowledge, regardless of whether AI models train on it or not.
- Search engines, content aggregators, and even social media algorithms already scrape and process data in ways similar to AI training.
- AI developers aren’t the only ones using public content — major corporations, ad agencies, and data firms have been doing it for years.
- This bill falsely assumes that laws can retroactively control what has already been absorbed into AI models.
- Case In point I’m publishing an entire backlog of content. Why? To prove a point. Once it’s out there’s nothing I can do to “protect” it.
Misconception #3: AI Can Accurately Reproduce Proprietary Work
If AI were capable of reproducing proprietary work word-for-word, it would be a direct copyright violation — but that’s not how AI works.
- AI models reconstruct information based on learned relationships, rather than memorizing exact phrases or images.
- If you ask AI to generate a scene in the style of Star Wars, it might create something influenced by Star Wars themes but not a direct copy of a copyrighted script or film.
The reality? AI isn’t copying — it’s interpreting.
The Real AI Copyright Challenge: Regulation That Actually Works
If lawmakers truly want to protect creators without stifling AI innovation, they need to focus on real AI literacy and forward-thinking policies, such as:
✅ Transparency in AI Training — Instead of lawsuits over dataset usage, AI companies should provide more accessible explanations of how models are trained. (See California AB 412)
✅ Fair AI Attribution — Rather than preventing AI from learning, platforms could implement automatic attribution systems that credit original creators when AI-generated works closely resemble known materials.
✅ User Control Over AI-Generated Content — Give creators more options to opt in or opt out of AI training datasets, similar to how Google allows websites to prevent indexing through robots.txt files.
✅ Preventing AI Monopolies — Instead of punishing AI research, laws should prevent large corporations from hoarding AI models, ensuring independent developers can build ethical AI solutions. (See ongoing AI copyright cases)
Why Lawmakers Are Really Pushing This Bill
Let’s be honest — this bill isn’t about protecting creators. It’s about control.
1️⃣ AI is decentralizing content creation. A single person using AI can now produce at the scale of an entire media company — and traditional industries don’t like that.
2️⃣ Gatekeepers are losing power. Before AI, creators relied on publishing houses, movie studios, and corporate platforms to distribute their work. AI changes that by giving individuals full creative control.
3️⃣ The legal system is trying to catch up — but failing. Instead of crafting adaptive AI policies, lawmakers are trying to fit AI into outdated copyright laws that were never designed for machine learning.
AI Isn’t the Problem — Ignorance Is
The real issue isn’t AI stealing from creators — it’s society misunderstanding AI’s potential.
📌 AI does not function like a giant copyright theft machine — it’s an adaptive learning system.
📌 This bill will not stop AI from evolving, nor will it prevent public data from being processed.
📌 The real opportunity lies in crafting policies that balance AI innovation with fair creator attribution.
Instead of reactionary laws that try to fit AI into a 20th-century copyright model, we need a real conversation about how AI and human creativity can work together in the 21st century.
If lawmakers actually want to help creators, they should focus on policies that protect their future — not ones that punish technological progress.
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