[ralfd]

> Red heads are less than 2% of the population and yet if you built a classification system that failed to identify them as people that wouldn't be seen as acceptable either.

So the solution would be to not identify _anyone_ as people? As a ginger I doubt that.

[foldr]

The solution would be to accept that the classification algorithm was too error probe to be usable for non-toy applications.

[ralfd]

We are going in circles. An error rate of 2% (heck! 20%!) would not make a classification unusable, that was what one of the grandfather posts was arguing.

https://adssettings.google.com/u/0/authenticated

Here Google can infer my gender and my age and personal interests just from my search history. I am sure this is not perfect either, it is still immensely useful for advertising.

[lordCarbonFiber]

Not the best example, given google lets you toggle your ad segments when they get them wrong and isn't baisising that guess based on physical presentation; the ad segment "male" is just a comment on the things you search and click on.

[natalyarostova]

Why are you imposing the error tolerances the user should have? Shouldn't that be their choice?

[lordCarbonFiber]

If someone wants to build and train their own model that assigns a binary gender based on pictures that's their own choice, and because of that freedom they can't force google to do it for them.

[natalyarostova]

Right, it's just that usually the concept with goods and services is that you're paying someone else to do it for you because you either can't or don't want to.

[foldr]

If I'm the author of the tool, I can impose whatever I want, no? It's not like people have a God-given right to have Google tag photos with genders for them.

[EvgeniyZh]

So the best argument to do is the fact that you have a legal right to?

[foldr]

The argument in this case is that the potential for misuse outweighs the potential benefits.