Comment by comsolo | Hacker News Reader

Comment by comsolo | Hacker News Reader

Another instance of SJW types causing problems in the coding world (although the vast majority of actual coders aren't on the SJW train).

The fact is gender can be determined by appearance in 99.5% of circumstances or more, which is probably higher than the overall accuracy of their ML labelling system.

The idea of "avoiding unfair bias" is highly vague and problematic since, theoretically nearly any statement or view beyond "particle X is at Y" type statements has a somewhat "unfair" bias reflecting the lens of the human experience, which has developed over millions of years of biology and thousands of years of culture.

Finally, seeing as the fact is that 99% of people would not be negatively affected by this feature remaining whatsoever, and the very slight negative impact on the other (intersex type) 1% or so is minimal at best, while the total impact of removing the feature is mildly annoying for many users, this seems a lot like irrational, hollow, virtue signalling.

>the other (intersex type)

The biggest group likely to be impacted is trans people, not intersex people.

Still less than 1% of the population, and that number could dwindle if and when treatment sorts out the underlying problems with the mental wiring causing the gender dysmorphia, and the current trend dies out.

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.

Trans people have been around forever, and aren't going away just to make image classification less complex. It's not a "trend", treatment options just happen to be more available and the internet makes communities more visible now.

> 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.

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

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.

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.

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

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.

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

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

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.

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.

Depends on the application. For something like approximating demographics of retail store traffic, this kind of issue would probably be fine. I don’t think anybody expects these models to be right 100% of the time anyway.

A more charitable interpretation would be that classifying gender is hard. It is so hard that humans have trouble classifying gender purely with sight. If humans have so much trouble, how can we teach machines?

Should we not teach machines to do things, or attempt to do things, if they can't do a better job than humans?

Classifying gender is easy. Humans do it correctly with near-perfect accuracy, typically failing only on edge cases. Furthermore, most cultures already bake in signaling of gender in various forms (Behavior, attire, etc).

Humans don't have much trouble, except against adversarial/outlier examples.

Can you provide some citations for what you’ve written?

First, please hold yourself to the same standard that you're holding me to. I see no citations to your (paraphrased) "identification of gender is exceedingly hard for humans". That goes heavily against most people's intuition and thus carries a higher burden of proof.

Now, 0.6% of Americans identify as transgender [http://williamsinstitute.law.ucla.edu/wp-content/uploads/How...]. That means using sex prediction alone, we can achieve a theoretical maximum of 99.4% accuracy here. This ignores the fact that transgenders often signal their gender via behavior or clothing, or attempt to obtain the physical characteristics of their assumed genders as well, both of which could further improve performance.

I asked for your help. Why did you see fit to get rude? What the hell is wrong with society that people like you think this is okay? Toxic responses like this ruin tech because instead of teaching you attack.

Loads of research suggests 7 year olds are about 70% accurate at predicting gender based off of pictures. It takes a couple of decades to get that into ranges you’ve cited. That doesn’t sound easy. If it is, I’d love to read about it.

Why be toxic?

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