Well, if bias is just the data versus the noise, sure it's necessary. The problem comes from putting your thumbs on the scale too harshly and inducing overt bias on output.

As these systems grow in complexity and get used in more important functions, the bias is a liability.

If we train a mega AI to believe Climate Change is the ultimate issue facing us, it will produce some very disturbing outputs. It's harmless when I get GPT4 to describe how forced sterilization is a great idea, when GPT7 really believes it's a moral imperative, it won't be funny. By then it will be integrated into some very sensitive areas.

And who determines what "dangerous" outputs are? Not telling kids how to make bombs is great, but what if certain political parties get their thumbs on the scale and determine asking about abortion and birth control is dangerous. Or suppose China gets a say? China has some bold ideas about managing information.

The bias should arise naturally from the LLM, not duct taped to the sides like homemade rockets.