4 ms·

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 ca

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.

foldr 7y ago
>the other (intersex type) The biggest group likely to be impacted is trans people, not intersex people.
- comsolo 7y ago
  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.
  - lordCarbonFiber 7y ago
    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.
    - ralfd 7y ago
      > 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 7y ago
        The solution would be to accept that the classification algorithm was too error probe to be usable for non-toy applications.
        ralfd 7y ago
        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 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 7y ago
        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 7y ago
        Why are you imposing the error tolerances the user should have? Shouldn't that be their choice?
        foldr 7y ago
        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.
hluska 7y ago
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?
- natalyarostova 7y ago
  Should we not teach machines to do things, or attempt to do things, if they can't do a better job than humans?
- esyir 7y ago
  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.
  - hluska 7y ago
    Can you provide some citations for what you’ve written?
    - esyir 7y ago
      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-Many-Adults-Identify-as-Transgender-in-the-United-States.pdf 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.
      - 7y ago

foldr 7y ago

>the other (intersex type) The biggest group likely to be impacted is trans people, not intersex people.

comsolo 7y ago
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.
- lordCarbonFiber 7y ago
  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.
  - ralfd 7y ago
    > 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 7y ago
      The solution would be to accept that the classification algorithm was too error probe to be usable for non-toy applications.
      - ralfd 7y ago
        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 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 7y ago
        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 7y ago
        Why are you imposing the error tolerances the user should have? Shouldn't that be their choice?
        foldr 7y ago
        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.

hluska 7y ago

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?

natalyarostova 7y ago
Should we not teach machines to do things, or attempt to do things, if they can't do a better job than humans?
esyir 7y ago
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.
- hluska 7y ago
  Can you provide some citations for what you’ve written?
  - esyir 7y ago
    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-Many-Adults-Identify-as-Transgender-in-the-United-States.pdf 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.
    - 7y ago