Bias is unavoidable with AI and that’s a Good Thing

Perspective — Jan 9, 2026

By Piers Fawkes — 7 min read

A persistent misconception about AI is the idea that we should aim to remove bias entirely. But bias isn’t a flaw—it’s the product of experience.

In theory, removing bias sounds reasonable. Bias is usually framed as distortion, unfairness, or error. If we could strip it away, we’d be left with something objective, neutral, and reliable.

In practice, that’s not how knowledge works: Bias isn’t something that appears only when systems go wrong. Bias is what allows systems, human or machine, to function at all. Every act of understanding involves selection. Every judgment involves prioritization. Every explanation depends on deciding what matters more than something else.

Humans do this instinctively. Experts, in particular, develop bias as a form of compression. After years of exposure to a domain, they learn which signals to trust, which patterns to ignore, and which relationships consistently explain outcomes. That bias isn’t a flaw. It’s the product of experience.

The Trap of Probabilistic Averaging

Large language models take a very different approach. They are designed to minimize bias by averaging across enormous datasets and optimizing for likelihood. As probabilistic systems, they are extremely good at producing answers that sound reasonable and balanced. They are much less good at committing to a point of view.

When everything is treated as equally important, nothing really is.

This is where many AI systems struggle in real-world use. They can generate competent summaries, but they hesitate where experts would commit. They flatten nuance precisely because they are trying to avoid bias rather than understand it.

Legible Bias as a Feature

In practice, organizations don’t need AI to be neutral. They need it to exercise reliable judgment. They need systems that can surface bias deliberately and on demand. Expert opinion is critical here, but too often AI systems treat a deeply held expert perspective as equivalent to a well-SEOed promotional article.

Sure, when bias is implicit and unexamined, it can be dangerous. When bias is explicit and structured, it becomes a feature. It allows systems to explain why they reached a conclusion. It allows teams to debate assumptions. It allows AI outputs to be audited, improved, and trusted rather than accepted blindly.

“Better AI comes from making bias legible. From encoding expert judgment in a way machines can use without pretending that judgment doesn’t exist.”

This is why I’ve become increasingly skeptical of the idea that better AI comes only from more data or larger models. Without structured context, scale simply amplifies averaging. It doesn’t produce insight.

When you’re building AI systems inside a brand or agency, the goal shouldn’t be to eliminate bias. The goal should be to design it, or source it, and apply it carefully.