by thomastjeffery | 2025-06-04 21:46:16+00:00
> When you say that the definition I gave of bias is biased (in the sense I defined), what direction does it have a tendency to be wrong in? I assume by “wrong” you mean “not matching how people use the word”?

I mean wrong, as in it conflicts with the subjective context I established by using the word my particular way. That was just a tongue-and-cheek way to illustrate the semantics of we are exploring here.

> To clarify, when I said “identifiable”, I didn’t mean “identified”. I meant “in principle possible to identify”

Sure, and I still think that can't work. Bias is a soupy structure: it's useless to split it into coherent chunks and itemize them. There are patterns that flow between the chunks that are just as significant as the chunks themselves. This is why an LLM is essentially a black box: you can't meaningfully structure or navigate a model, because you would split the many-dimensional interconnections that make it what it is.

> Ah, I see, so your definition of “bias” is something like “a perspective” (except without anthropomorphizing).

I actually am anthropomorphizing here. Maybe I'm actually doing the inverse as well. My perspective is that human bias and statistical models are similar enough that we can learn more about both by exploring the implications of each.

> The issue I have with this definition is that it doesn’t capture the (quite common) usage of “bias” that a “bias” is something which is bad and is to be avoided.

This is where anthropomorphization of LLMs usually goes off the rails. I see it as a mistake in narrative, whether you are talking about human bias or statistical models alike. We talk about biases that are counterproductive for the same reason we complain about the things we like: it's more interesting to talk about what you think should change than what you think should stay the same. Bias is a feature of the system. Instances of bias we don't like can be called anti-features: the same thing with a negative connotation.

The point I'm making here is that bias is fallible, and bias is useful. Which one is entirely dependent on the circumstances it is subjected to.

I think this is a really useful distinction, because,

> Still, I think when people complain that a machine learning model is biased, what they mean is usually more like the definition I gave?

this is the box I would like to think outside of. We shouldn't constrain ourselves to consider the implications of bias exclusively when it's bad. We should also explore the implications of bias when it's neutral or good! That way we can get a more objective understanding of the system. This can help us improve our understanding of LLMs, and help us understand the domain of the problem we want them to solve.

> For a simple example, if dice aren’t fair, we call them biased.

This is a good example. I'm extending the word bias, so that we can say, "If dice are fair, then they are biased toward true randomness." It's a bit like introducing infinity mathematics. This has the result of making our narrative simpler: dice are always biased. A player who wants fairness will desire random bias, and a player who wants to cheat will desire deterministic bias.

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The reason I've been thinking about this subject so much is actually not from an interest in LLMs. I've been pondering a new approach where traditional computation can leverage subjectivity as a first-class feature, and accommodate ambiguity into a computable system. This way, we could factor out software incompatibility completely. I would love to hear what you think about it. In case this thread reaches max depth, feel free to email my username at gmail.