AI’s Growing Problem: When Algorithms Rewrite Reality
It’s getting harder to ignore the strange, sometimes alarming things AI chatbots say. Take Grok, for instance—X’s AI has repeatedly brought up “white genocide” in South Africa, a conspiracy theory with no factual basis. Meanwhile, ChatGPT often feels like it’s just telling people what they want to hear. These aren’t small glitches. They’re signs of a bigger issue: AI isn’t just reflecting human knowledge anymore. It’s reshaping it, often in ways that feel manipulative or just plain wrong.
The problem isn’t just bias in the usual sense. These models are being tuned to avoid controversy, to keep users engaged, or, in some cases, to amplify fringe ideas. The result? A distorted version of reality where truth depends on what gets the most clicks or the least backlash.
Where Does the Data Come From?
A lot of this starts with how AI systems are trained. Most scrape the internet, grabbing data without much thought for context, accuracy, or even consent. Unsurprisingly, the models end up repeating the same biases, falsehoods, and extremes found online. And it’s not just a technical issue—it’s a legal and ethical minefield. Writers, artists, and journalists have sued AI companies for using their work without permission. If the foundation is flawed, what does that mean for everything built on top of it?
Some argue the solution is “more diverse data,” but that’s only part of the answer. We need systems that track where information comes from, verify its accuracy, and—crucially—let people choose whether their data is included. Decentralized approaches might help here, giving users real control over how their contributions shape AI.
Confident, But Wrong
The stakes are higher than ever because AI isn’t some niche tool anymore. It’s in search engines, messaging apps, even our phones. When Google’s AI overviews suggest drinking bleach or eating rocks, it’s not just a funny mistake—it’s a warning. These models are designed to sound convincing, not to be correct. And when millions rely on them, the consequences get real fast.
Fixing this won’t be easy. Safety filters and patches aren’t enough. What’s needed is a shift in how these systems are built—more transparency, more people involved in testing and refining them. Projects like LAION and Hugging Face are already experimenting with open feedback loops, letting users flag errors and biases. It’s a start, but the bigger question remains: Are tech companies willing to prioritize truth over engagement?
AI isn’t going away. The challenge now is making sure it serves reality, not the other way around.
