[bitL]

There is a strong push for "fairness", see e.g. "Toronto Declaration". I think all it would do is completely halt progress of AI and install bureaucracy to the lowest decision levels, paralyzing whole ML research. Nobody seems to think that we are in a clash of different cultures with different sensitivities and there is no single common platform for stating what is "fair". I am worried the loudest voice would set the trend and we will have some insanity enforced all the way down. There are even calls to ban "blackbox" ML, basically allowing only trivial parts in any kind of decision making.

If members of my nation get drunk more often than some other, while it's offensive to say I am a 34% drunkard, on average it might hold; instead of forbidding this type of inference I'd rather rely on more signals to figure out what kind of person I am specifically for individualized decisions. They bypass this problem by adding "risky behavior" not contained in the input dataset so they just decide to model it as a hidden variable of Bayesian inference, where "risky behavior" might be correlated with ethnicity and red car anyway, just not visible outside. So if my nation is 34% drunkard but neighboring is only 11%, the conditional probability will likely be higher for my nation anyway, but obfuscated by the use of Bayesian hidden state. I am not sure why would that improve fairness.

[barry-cotter]

> There is a strong push for "fairness", see e.g. "Toronto Declaration". I think all it would do is completely halt progress of AI and install bureaucracy to the lowest decision levels, paralyzing whole ML research.

It would only paralyze those who paid attention to the Toronto Declaration. You’re right because you can’t make ML fair because the universe isn’t fair, that’s a property of human judgements about facts. The facts remain the same regardless of ones feelings.

https://www.chrisstucchio.com/pubs/slides/crunchconf_2018/sl...

AI Ethics, Impossibility Theorems and Tradeoffs

[candiodari]

Except any 2 humans don't have matching ideas about what's fair, which means that they're both unfair from eachother's perspective.

Humans are in reality much less fair than algorithms.

[taneq]

> there is no single common platform for stating what is "fair".

This is the crux of the issue and as always, most people seem to miss it. Often “fair” is used as shorthand for “does what I think is right”.

[perl4ever]

"forbidding this type of inference"

Isn't this just a misleading way to say "holding a certain causal belief"? Why exactly would that be a bad thing? If you reject one set of causal beliefs, you necessarily hold a different set.

[TeMPOraL]

Some beliefs are correlated with reality, others don't. If GP's assertion about 34% more drinking on average is true, then rejecting it isn't "holding a different set of beliefs", it's just being wrong.

If there's an issue worth pursuing here, it's educating people to stop using average population statistics to rate individuals from populations. Usually the variance within a population makes population-level statistics useless for evaluating individuals.

[perl4ever]

Rejecting the causal relationship is not the same as rejecting the correlation, right? Why can't (or shouldn't) one separate the two?

[TeMPOraL]

You're right in principle, but the point here is about the reasons for rejecting a casual model. The issue people seeking fairness in statistics run into is rejecting models based on what ought to be, instead of what is. A casual model can be totally unfair, and yet also correct (insofar an approximation is considered correct).

Taking the example from our parallel discussion, if the data says being male is correlated with risky driving, and it seems to fit the casual model of "male -> risky", it would be wrong to reject it just on the grounds of "we're using this model to set insurance rates, so by penalizing males, the model is sexist". It may be that you can come up with a better casual model explaining the correlation - say, cultural history and path dependence - but until you can, rejecting a fitting model based on "it's unfair, reality ought not to be so" is just wrong.