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0 pointsmrow849y ago0 comments

To correct biased measurements (in a careful way) you need

1. Enough knowledge about the structure of the bias to be able to devise a model for it.

2. Some measurements from which to fit the model, with errors that are uncorrelated with the errors in your original data.

These things are not always easy to obtain, even in relatively mundane settings. It is also a distinctly non-automatic procedure - it requires someone to decide that a bias exists, to model it, obtain the relevant data, and fit the bias correction model, all before they can begin to obtain unbiased (or probably just less-biased) measurements.

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yummyfajitas9y ago

I'm not making the claim that an algorithm magically fixes everything. I'm claiming that sometimes they do which makes bias less likely to be present in the ML model.

You don't need a human data scientist to decide bias exists, model it and fix it at all. If you read the post I linked to, you can observe a synthetic example of linear regression (with redundant encodings) accidentally fixing bias.

So yes, if your model is expressive enough and you have sufficient data, it will automatically fix bias. Is it really shocking that an algorithm which is good at finding hidden patterns will find a hidden pattern?

mrow84OP9y ago

I don't really understand that claim. You are explicitly adding a bias that is linearly dependant on your race variable, and then allowing your regression to recover that bias by introducing noisy measurements of race (which you as the modeller knew was the thing causing the bias). As you say, that is unsurprising.

That result does not, however, address my point, which is that if the structure of the bias is difficult to understand, or perhaps even just difficult to model, and if relevant measurements (with errors that are uncorrelated with your original errors) are unavailable, then bias correction is essentially impossible.

yummyfajitas9y ago

The point is that the bias is linear, and my model is linear, so the model fixes things. The example is synthetic (so we could know what the right answer is and check if we recover it) so of course I put everything in.

In the linked article, I explicitly reference a real world case where the same linear model was used to discover that grades and test scores are biased in favor of blacks: http://ftp.iza.org/dp8733.pdf

In more complicated situations, the bias would need to be amenable to detection by a neural network, an SVM or random forest. The entire purpose of models like this is that lots of hidden patterns are detected.

Even if relevant measurements are unavailable, one can use redundant encoding to fix bias. Delip Rao explains redundant encoding here, for example, though he is more concerned that ML models might learn facts he wants to remain hidden: http://deliprao.com/archives/129

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0 pointsmrow849y ago0 comments

To correct biased measurements (in a careful way) you need

1. Enough knowledge about the structure of the bias to be able to devise a model for it.

2. Some measurements from which to fit the model, with errors that are uncorrelated with the errors in your original data.

0 comments

yummyfajitas9y ago

I'm not making the claim that an algorithm magically fixes everything. I'm claiming that sometimes they do which makes bias less likely to be present in the ML model.

mrow84OP9y ago

yummyfajitas9y ago

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j / k navigate · click thread line to collapse