Data Incest: When AI Breeds with Itself
Today, we’re diving into a problem that’s as bizarre as it is concerning: data incest. No, this isn’t some dystopian sci-fi horror plot. It’s a real issue creeping into AI and machine learning.
And trust me, it’s as messy as it sounds.
Imagine this: you’re a large language model (LLM) like ChatGPT, Deepseek, or one of their AI cousins. You’ve been trained on a massive diet of human-generated text—books, articles, tweets, or Reddit. But as you mature, you start generating your own content. And here’s where things get weird: some of that content ends up back in your training data.
Congratulations, you’ve just entered the world of data incest, where AI models are fed their own outputs, creating a feedback loop that gets uglier over time.
Let me break this down, because this is a looming crisis for AI, and it’s time we talk about it.
What Is Data Incest?
Data incest is when AI models are trained or fine-tuned on data they themselves generated. To avoid any unsettling comparison, think of it as a snake eating its own tail, except the snake is a multi-billion-parameter neural network, and the tail is a never-ending stream of AI-generated blog posts, tweets, and most of the ads that target you on a daily basis.
At first, this might not seem like a big deal. AI-generated content is often coherent, grammatically correct, and sometimes even insightful. But, as we know, AI models are not perfect. They make mistakes, they hallucinate facts, and they inherit biases from their training data. When you feed these imperfect outputs back into the model, you’re amplifying those flaws. Over time, the model starts to believe its own nonsense, and the quality of its outputs degrades. This is what researchers call model collapse, a fancy term for “the AI is eating itself into oblivion.”
So now, you might be thinking, “Okay, why does this matter to me? I’m not training AI models in my basement.” Well, you are using those models. Moreover, data incest has far-reaching consequences that affect everyone who uses the Internet, relies on AI tools, or simply enjoys a well-written article.
Here are some facts that are a direct consequence of the data incest issue.
Internet: an AI Echo Chamber Imagine a future where a significant portion of online content is generated by AI. Actually, we are not that far.
Now imagine that same content being used to train the next generation of AI models. We are also not that far.
What you get is a feedback loop where AI-generated content dominates the Internet, and human-generated content becomes a rare relic of the past.
The result? An internet filled with repetitive, bland, and increasingly inaccurate information. Goodbye creativity.
If there’s one thing we learned from statistics is that bias gets worse, not better. AI models already inherit biases from their training data. When you train a model on its own outputs, those biases get amplified. For example, if an AI model has a tendency to generate gender-stereotyped content, feeding that content back into the model will reinforce those stereotypes. Apply this very mechanism to politics and you get the point.
Over time, the model becomes a caricature of its worst tendencies, and fixing it becomes exponentially harder.
Another massive risk of the data incest effect is the loss of ground truth, the reliable, human-generated data that keeps AI models anchored to reality. As AI-generated content proliferates, the proportion of real, human knowledge in training datasets shrinks.
This means that models become increasingly detached from reality, generating content that sounds plausible but is factually incorrect. A lot more than the hallucinations we have learned to accept in the last two years.
Imagine an AI writing a history essay where Napoleon invades the moon. Sounds fun, until you realize people might actually believe it. After all, some people believe the Earth is flat, vaccines are lethal, and that Elon Musk is the richest genius in the world.
How Did We Get Here?
Data incest isn’t some far-off dystopian scenario. As a matter of fact, it is already happening. As AI-generated content floods the Internet, it’s becoming harder to distinguish between human and AI-generated text. I tell you more. Some of that AI-generated content is already being used to train new models.
Remember that game kids played in the '90s (and probably way before that) where one child whispered a story into another’s ear, who then passed it along to the next, and so on? By the time it reached the fourth kid, the original story about a brave knight rescuing the blonde princess had somehow morphed into a talking pineapple declaring war on socks :D :D
The problem is compounded by the fact that AI models are often fine-tuned on niche datasets, which may include AI-generated content. For example, if you’re training a model to write legal documents, you might use a dataset of legal texts, some of which could have been generated by an AI. Over time, this creates a feedback loop where the model becomes increasingly specialized in generating content that’s similar to its own outputs, losing the diversity and richness of the original training data.
“Ok Frag, this is a catastrophe. What should we do?” I hear you.
Fine, let’s talk solutions. Because while data incest is a serious problem, it’s not insurmountable. Here are some ways we can mitigate the risks.
Curation, curation, curation. The first line of defense against data incest is data curation. This means carefully selecting and filtering the data used to train AI models, ensuring that it’s high-quality, diverse, and free from AI-generated content.
Despite the fact that you should never take advice from me about how to do gardening, this is really like tending a garden and choosing the best seeds (data), carefully planting them in fertile soil (training models), and regularly pruning away the weeds (low-quality or biased data) to ensure the plants (AI outcomes) grow strong and healthy.
Power to the… humans To prevent models from becoming too inbred, we need to regularly update their training data with fresh, human-generated content. This could include books, articles, and other sources of reliable information. It’s like giving the model a breath of fresh air after spending too long in a stuffy room. Believe me, that happens to me all the time. And gosh, I become a lot more creative after a fresh walk in the park.
AI cops and detection tools Identifying AI-generated content in the first place is crucial. Researchers are working on tools to detect AI-generated text, which could be used to filter it out of training datasets. It’s like a spam filter, but for AI nonsense. Watch “Humans vs Bots: Are you talking to a machine right now?”
Human-in-the-Loop Systems Another approach is to involve humans in the training process. For example, humans could review and validate the data used to train AI models, ensuring that it’s accurate and free from biases. To be fair, this is partially happening with reinforcement learning with human feedback (RLHF). But as AI-generated content scales, relying solely on humans might not be sustainable.
Regular Audits and Evaluations We need to regularly audit and evaluate AI models to ensure they’re not drifting into nonsense. This could involve testing the models on a variety of tasks and datasets to measure their performance and identify any signs of degradation.
And please, don’t even think about cheating by finetuning your old models on new benchmarks. Because, of course, no one has noticed that some “Open” and “advanced” AI models have somehow gotten a lot dumber over the past year 🤓😵💫
Data incest is a wake-up call for the AI community. It’s a reminder that we can’t just sit back and let AI models run wild. We need to be proactive in addressing the risks and ensuring that AI remains a force for good.
The next time you read an article or chat with an AI, ask yourself: is this the product of human creativity or just an AI trapped in a loop of its own making?
Just kidding, you won’t do that.