3 ms·

ML, even when perfect, learns the world as it is, not as people want it to be. There's nothing inherently sexist about a machine noticing that the background of

ML, even when perfect, learns the world as it is, not as people want it to be. There's nothing inherently sexist about a machine noticing that the background of an image can be used to identify a class label, e.g. noticing that a mug is more likely to contain coffee than tea in a coffee shop, but given how shitty we have been to each other, some correlations are considered taboo. The only way for machines to learn our taboos is to explicitly teach them.

jononor 9y ago
Machine learning is only as good your training set. Readily available training sets, from instance based on what users volunteer to hand out in social networks, may have certain skews due to how they are collected. For supervised learning methods another possible source of bias is in the labeling process.
rixed 9y ago
Have you read the article? The problem is not that ML reproduces the bias that is present in the training set, it is that it amplifies it (a lot).
- Eridrus 9y ago
  The article is garbage, but anyway. If you see that an algorithm has issues with imbalanced classes, you could try to address that, or you could frame your problem as sexism, write a paper with that in the headline and then call Wired. And in any case, if you read the paper, they examine a single algorithm (Conditional Random Fields) on two datasets (which Wired extrapolates to the entire field), and their own solution is to add constraints saying that it should preserve the ratio of woman:man cooking as in the original dataset. And while there is no loss of accuracy, it also has no improvement, so it's just shifting the errors around. And it has absolutely no analysis of why a CRF would exhibit these properties anyway. But this is kind of my point, even after you solve these issues that arise from class imbalance (which ML practitioners & researchers are highly motivated to solve these already because they lead to better average performance), you are still left with a bias that is taboo that society will say must be fixed, which cannot be fixed by simply more accurate ML or more accurate data.

jononor 9y ago

Machine learning is only as good your training set. Readily available training sets, from instance based on what users volunteer to hand out in social networks, may have certain skews due to how they are collected. For supervised learning methods another possible source of bias is in the labeling process.

rixed 9y ago

Have you read the article? The problem is not that ML reproduces the bias that is present in the training set, it is that it amplifies it (a lot).

Eridrus 9y ago
The article is garbage, but anyway. If you see that an algorithm has issues with imbalanced classes, you could try to address that, or you could frame your problem as sexism, write a paper with that in the headline and then call Wired. And in any case, if you read the paper, they examine a single algorithm (Conditional Random Fields) on two datasets (which Wired extrapolates to the entire field), and their own solution is to add constraints saying that it should preserve the ratio of woman:man cooking as in the original dataset. And while there is no loss of accuracy, it also has no improvement, so it's just shifting the errors around. And it has absolutely no analysis of why a CRF would exhibit these properties anyway. But this is kind of my point, even after you solve these issues that arise from class imbalance (which ML practitioners & researchers are highly motivated to solve these already because they lead to better average performance), you are still left with a bias that is taboo that society will say must be fixed, which cannot be fixed by simply more accurate ML or more accurate data.