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Training Data

In any legal dispute involving an AI, there is one piece of evidence that matters more than any other: the training data. It is the foundation upon which the entire system is built. The AI’s capabilities, its flaws, its biases, and its legal liabilities are all a direct reflection of the data it was fed. An AI model is nothing more than a compressed, statistical representation of its training data.

Analogy: A Human Life vs. a Data Life

The AI industry loves to use human analogies for “learning.” This is a deliberate, self-serving misdirection. Consider the difference between how a human learns and how an AI is trained.

  • A Human’s “Training Data”: A human’s experience is a rich, multi-sensory stream of information filtered through a lifetime of context. We see, hear, and read things, but we also forget, dream, and form our own independent ideas. We have a concept of “self” and “other,” and we understand that a book in a library is someone else’s work.
  • An AI’s Training Data: An AI’s experience is a firehose of raw text and pixels, stripped of all context. It does not “read” a book; it is shown a sequence of digital tokens. It does not “see” a photograph; it is fed a grid of pixel values. It has no context, no memory, and no self. It is a perfect, unthinking accumulator. It makes no distinction between a copyrighted novel and a public domain poem, or between a private medical record and a public blog post. It is all just data to be statistically analyzed.

To say that an AI “learns” like a human is a category error. A human learns from the world; an AI model is a compressed version of the data it was shown.

Because the model is its data, the legal character of that data is paramount.

  1. The “Trade Secret” Shell Game: AI companies will fight to the death to prevent discovery of their full training datasets, claiming they are a protected trade secret. This argument is often a sham. While the exact composition and weighting of a dataset might have some proprietary value, the primary reason for secrecy is to hide mass-scale copyright infringement and the ingestion of private data. Forcing disclosure, or using technical means to prove the contents of the data in spite of secrecy, is the central challenge for plaintiffs.

  2. Known “Tainted” Datasets: The AI research community has produced a number of massive, publicly-available datasets that are known to be built from infringing material. These include Common Crawl (a crawl of the entire web, including copyrighted sites), The Pile (a massive text dataset known to include pirated books), and LAION (a dataset of billions of image-text pairs that is central to image model lawsuits). The use of any of these datasets is a de facto admission of training on copyrighted works.

  3. There is No “Clean Room”: There is no such thing as a large-scale, clean-room training dataset for a powerful, general-purpose AI. The capabilities of models like GPT-4 are a direct result of the breadth and depth of the data they were trained on, which by necessity includes the copyrighted heart of the creative internet. Any claim to the contrary is a fiction.

The entire legal battle over generative AI is a battle over the training data. Every other technical detail—the model architecture, the alignment process, the use of synthetic data—is a sideshow. The core question is, and always will be, “What did you train it on, and did you have the right to use it?”