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Yesterday, I ended up in a debate where the position was "algorithmic bias is a data problem". I thought this had already been well refuted within our research community but clearly not. So, to say it yet again -- it is not just the data. The model matters. 1/n
elaborate?
He can’t
But disparate impact is in itself a form of discrimination which is an implicit bias. Algorithmic bias is always going to be in form of disparate impact because it is generally unintended discrimination.
No, disparate impact per se is not discrimination. Ask any civil rights lawyer. (And, in any case, "unintended discrimination" is arguably an oxymoron.)
So if I were to train for approving credit cards, hiring/recruiting screening, auto-rental application decision making and end up constantly screwing a sub-class, that's not disparate impact because I didn't intend for that effect? Pedro go learn English.
Not quite. I make clear that disparate impact coincides with notions of algorithmic bias when protected attributes are underrepresented. This is frequently the case in the real world. Here, model choices can amplify algorithmic bias.
The issue of sample sizes from each group has almost NOTHING to do with the algorithmic bias. If you have k-protected attributes (instead of one), say [race, gender, age, nationality,..] then you can NEVER have enough samples from each perceivable 2^k subgroups.
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