AI models trained on sources like Wikipedia inherit their biases. Wikipedia's policy of not allowing citations from leading conservative publications means these viewpoints are systematically excluded from training data, creating an inherent left-leaning bias in the resulting AI models.

When AI systems are trained on historical data, such as past hiring or policing records, they learn and perpetuate existing societal biases. This creates a dangerous illusion of objectivity, where discriminatory outcomes are presented as neutral, data-driven "predictions" by an algorithm.

While AI can inherit biases from training data, those datasets can be audited, benchmarked, and corrected. In contrast, uncovering and remedying the complex cognitive biases of a human judge is far more difficult and less systematic, making algorithmic fairness a potentially more solvable problem.

Hands-on AI model training shows that AI is not an objective engine; it's a reflection of its trainer. If the training data or prompts are narrow, the AI will also be narrow, failing to generalize. This process reveals that the model is "only as deep as I tell it to be," highlighting the human's responsibility.

Richard Sutton, author of "The Bitter Lesson," argues that today's LLMs are not truly "bitter lesson-pilled." Their reliance on finite, human-generated data introduces inherent biases and limitations, contrasting with systems that learn from scratch purely through computational scaling and environmental interaction.

AI models are not optimized to find objective truth. They are trained on biased human data and reinforced to provide answers that satisfy the preferences of their creators. This means they inherently reflect the biases and goals of their trainers rather than an impartial reality.

AI models trained on scientific literature face a hidden challenge: author interpretation bias. When extracting data, researchers found that numerical data in graphs often contradicts the authors' own textual interpretation of those same graphs, introducing a significant source of error and noise into datasets.

A comedian is training an AI on sounds her fetus hears. The model's outputs, including referencing pedophilia after news exposure, show that an AI’s flaws and biases are a direct reflection of its training data—much like a child learning to swear from a parent.

When tested with sociological surveys, AI models consistently align with the values of rich, secular, and self-expressive societies. This demonstrates they are not neutral tools but products of a specific cultural milieu—primarily Western and socially liberal—reflecting the data they were trained on.