4 ms·

I wish this machine bias wasn't always presented in such divisive terms as race and "disadvantaged groups". It can affect anybody. If you happened to develop a

I wish this machine bias wasn't always presented in such divisive terms as race and "disadvantaged groups". It can affect anybody. If you happened to develop a writing style that looks like typical bad essay writers' style, then you could be hurt by bias in the grading.

crooked-v 7y ago
Anyone can be hurt by bias, but minority groups are the obvious ones to be most likely statistically affected by it, making them the most obvious red flag for this kind of situation.
fzeroracer 7y ago
If an image processing algorithm fails to recognize black people or worse, profiles them, how else should this be described but in terms of race? If you don't talk about the actual problem, how can you possibly expect to solve it?
- lopmotr 7y ago
  There are many classes of people who have problems of discrimination. Short, ugly, ginger, etc. The intersections of all those classes are so numerous that everybody will have some disadvantage. But it won't be apparent unless you define their class and measure it.
  - crooked-v 7y ago
    That's just substituting in smaller or harder-to-define minority groups, though.
- Nasrudith 7y ago
  In terms of the technical effect that causes it as opposed to sociological. The point then isn't that the algorithm hates black people or the programmers are racist (even if they were they would likely find it hard to train it to specifically exclude accurately without major side effects), but that their training set or analysis is flawed - potentially even if it is representative. Even if the results are problematic trying to address hate which isn't there won't be helpful. Call for better vetting procedures instead or that it clearly isn't ready for the proposed application. Say it gets the highest accuracy on a set of photos of non-people images and a set of people which represents the exact ethnic make up all tagged just as "has people" or "doesn't have people". Going with something biased towards the most populous groups would get the best results quickest. Talking about say image tagging to ensure automated performance checking across various characteristics could be productive on the other hand.

crooked-v 7y ago

Anyone can be hurt by bias, but minority groups are the obvious ones to be most likely statistically affected by it, making them the most obvious red flag for this kind of situation.

fzeroracer 7y ago

If an image processing algorithm fails to recognize black people or worse, profiles them, how else should this be described but in terms of race? If you don't talk about the actual problem, how can you possibly expect to solve it?

lopmotr 7y ago
There are many classes of people who have problems of discrimination. Short, ugly, ginger, etc. The intersections of all those classes are so numerous that everybody will have some disadvantage. But it won't be apparent unless you define their class and measure it.
- crooked-v 7y ago
  That's just substituting in smaller or harder-to-define minority groups, though.
Nasrudith 7y ago
In terms of the technical effect that causes it as opposed to sociological. The point then isn't that the algorithm hates black people or the programmers are racist (even if they were they would likely find it hard to train it to specifically exclude accurately without major side effects), but that their training set or analysis is flawed - potentially even if it is representative. Even if the results are problematic trying to address hate which isn't there won't be helpful. Call for better vetting procedures instead or that it clearly isn't ready for the proposed application. Say it gets the highest accuracy on a set of photos of non-people images and a set of people which represents the exact ethnic make up all tagged just as "has people" or "doesn't have people". Going with something biased towards the most populous groups would get the best results quickest. Talking about say image tagging to ensure automated performance checking across various characteristics could be productive on the other hand.