There was a great comment here discussing the Pathetic fallacy, and how we humans apply it to machine learning systems. (It's now deleted and I don't know why.) Specifically, we treat machine learning systems as anthropomorphic, and assume they will reproduce our biases.
In reality, ML systems will generally correct human biases. And by bias, I really do mean bias in the statistical sense - systematically getting things wrong in a particular direction.
Now this article does a great job of explaining how ML systems might understand the meaning of words, and that meaning may contain bias. However, such a system is merely an input into a separate system which actually makes decisions based on those inputs. Extracting meaning from text makes no decisions of it's own. If that later system wants to make accurate decisions, then the best way to do that is to correct for the aforementioned bias, assuming that bias is really bias as opposed to just a correct but undesirable belief about the world [1].
I wrote a blog post a while back that goes into this idea with a bit more math, and which demonstrates some real world "learning" algorithms (mostly linear regression) actually correcting biases: https://www.chrisstucchio.com/blog/2016/alien_intelligences_...
One problem you will run into here is that political use of the word "bias" isn't any sort of statistical claim; it's often just used to mean that something violates vague social expectations about what information is "acceptable" to use when making decisions. ML algorithms don't care; they will make the best possible decision based on the information they have, even if their though process (so to speak) is "biased" in the political sense that it may use gender, race, nationality, etc. to help make optimal decisions.
Yes, unfortunately the term is overloaded. "Bias" can also mean "making correct decisions that I wish were incorrect". That's why I explicitly defined "bias".
In the past era, e.g. 1980-2010, it was possible to use vague emotive language to support all kinds of disparate things. As a concrete example that I touch on in my post, and since racism is the loaded undercurrent of this example, we like to pretend that eliminating racial or sexual bias (in the sense of making wrong decisions) will get us proportional representation.
Algorithms are bringing us to an age where analytic philosophy is becoming really important. You can tell an algorithm to give you proportional representation, or you can tell it to be racially/sexually unbiased. But the algorithm will reflect reality and reality may not agree with your assumptions; you can't assume that asking for one will give you the other. So now we get into trolley problems: how much meritocracy/equal opportunity will you sacrifice to get proportional representation?
Unlike before, this is now a choice you need to explicitly and openly state and acknowledge.
https://en.wikipedia.org/wiki/Pathetic_fallacy
In reality, ML systems will generally correct human biases. And by bias, I really do mean bias in the statistical sense - systematically getting things wrong in a particular direction.
Now this article does a great job of explaining how ML systems might understand the meaning of words, and that meaning may contain bias. However, such a system is merely an input into a separate system which actually makes decisions based on those inputs. Extracting meaning from text makes no decisions of it's own. If that later system wants to make accurate decisions, then the best way to do that is to correct for the aforementioned bias, assuming that bias is really bias as opposed to just a correct but undesirable belief about the world [1].
I wrote a blog post a while back that goes into this idea with a bit more math, and which demonstrates some real world "learning" algorithms (mostly linear regression) actually correcting biases: https://www.chrisstucchio.com/blog/2016/alien_intelligences_...