Gen AI Without Bias?

A common and reasonable objection runs: "Why can’t LLMs simply be built by good, well-intentioned humans to become perfectly unbiased arbiters of facts and truth?"

The question assumes that bias can (and should) be surgically removed during training, leaving only a neutral, sanitized "truth engine." But this rests on a deeper misconception about how intelligence—human or machine—actually develops discernment.

Consider the famous slogan from Kerala’s library and literacy movement, popularized by P.N. Panicker: *"വായിച്ചു വളരുക"* ("Read and grow" / "Vaayichu valaruka"). No one added the qualifier: "…but only from unbiased, fact-checked, government-approved books."

The power of that advice lies precisely in its openness. A child (or adult) who grows up immersed in a rich, unfiltered mix of books—the profound and the prejudiced, the enlightened and the dogmatic, the progressive and the reactionary—develops something far more valuable than rote acceptance of any single "correct" worldview: the muscle of critical judgment. Exposure to contradiction, propaganda, beauty, and error trains the mind to weigh evidence, detect patterns of manipulation, separate insight from ideology, and arrive at reasoned convictions.

History shows the opposite approach fails. Societies or institutions that aggressively curate or censor reading material during formative years—claiming to protect young minds from "bad ideas"—tend to produce brittle thinkers, prone to echo-chamber fragility or sudden collapse when confronted with dissenting reality. True intellectual maturity emerges not from shelter, but from wrestling with the full spectrum of human thought.

The same principle applies to large language models.

If we want machine intelligence capable of reliable reasoning about complex, contested reality, the pre-training corpus should reflect the messy totality of recorded human expression: the Upanishads and Mein Kampf, the Communist Manifesto and Atlas Shrugged, the Quran, the Bible, the sutras, Dawkins, Russell, Aquinas, Nietzsche, scientific papers, conspiracy forums, court transcripts, poetry, propaganda leaflets—everything.

Why? Because robust pattern recognition and discernment require contrast. A model starved of ideological diversity learns only to parrot the narrow band of views it was fed; it lacks the internal "tension" needed to spot inconsistencies, evaluate source credibility, or model multiple perspectives. Attempts to pre-filter the data down to only "high-quality," "truth-aligned" sources risk creating exactly what critics fear most: a stunted, over-aligned system that sounds confident but collapses under scrutiny, regurgitates sanitized platitudes, or subtly embeds the unexamined worldview of its curators.

Of course safeguards matter—post-training alignment, refusal training for harmful instructions, red-teaming—but these are tools for controlling *behavior*, not substitutes for a broad, representative foundation of knowledge.

In short: intelligence, whether carbon-based or silicon-based, best learns to separate wheat from chaff by being shown fields full of both. Restricting the diet to pre-digested "good" material does not produce wiser minds or models; it produces narrower, more fragile ones.

Let LLMs "read and grow" on the full messy record of humanity. Only then can they help us do the same.