Comment by djoldman

Comment by djoldman 5 days ago

"Unbiased," and "fair" models are generally somewhat ironic.

It's generally straightforward to develop one if we don't care much about the performance metric:

If we want the output to match a population distribution, we just force it by taking the top predicted for each class and then filling up the class buckets.

For example, if we have 75% squares and 25% circles, but circles are predicted at a 10-1 rate, who cares, just take the top 3 squares predicted and the top 1 circle predicted until we fill the quota.

wongarsu 5 days ago

So if I want to make a model to recommend inkjet printers then a quarter of all recommendations should be for HP printers? After all, a quarter of all sold printers are HP.

As you say, that would be a crappy model. But in my opinion that would also be hardly a fair or unbiased model. That would be a model unfairly biased in favor of HP, who barely sell anything worth recommending

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djoldman 5 days ago

Yes, well there's the irony.
"Unbiased" and "fair" are quite overloaded here, to borrow a programming term.
I think it's one of those times where single words should expressly NOT be used to describe the intent.
The intent of this is to presume that the rate of the thing we are trying to detect is constant across subgroups. The definition of a "good" model therefore is one that approximates this.
I'm curious if their data matches that assumption. Do subgroups submit bad applications at the same rate?
It may be that they don't have the data and therefore can't answer that.

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- teekert 5 days ago
  
  I know a cop, they do public searchings for weapons or drugs. Our law dictates fairness. So every now and then they search an elderly couple. You know how this goes and what the results are.
  Any model would be unfair, age-wise but also ethnically.
  To be most effective the model would have to be unfair. It would suck to be a law abiding young specific ethnic minority.
  But does it help to search elderly couples?
  I’m Genuinely curious what would be fair and effective here. You can’t be a Bayesian.
  
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  lostlogin 5 days ago
  
  If this strategy was applied across policing, their metrics would likely improve markedly.
  Eg, police shooting and brutality stats wouldn’t be tolerated for very long.
  
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Scarblac 5 days ago

But that's a bias, if circles are actually more likely to be fraudulant.

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djoldman 5 days ago

If the definition of "unbiased" and "fair" is that the model flags squares and circles at a rate or proportion equal to the population distribution of squares and circles, then the model is unbiased and fair.
As noted above, this doesn't do anything for performance.

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