2 ms·
Perfect example of how not proactively making an algorithm anti-racist can yield results that end up being racist. If you train a machine learning algorithm on
by slg 6y ago
Perfect example of how not proactively making an algorithm anti-racist can yield results that end up being racist. If you train a machine learning algorithm on photos with predominantly white faces, your algorithm is going to think people with lighter skin are more important thank people with darker skin.
- xyzzy_plugh 6y agoWhy are people downvoting this? The parent is correct. If you don't test your models adequately then they'll have holes like this one.
- slg 6y agoHN doesn't like talking about race or numerous other "controversial" topics. This story is already flagged off the front page.
- rvz 6y ago> HN doesn't like talking about race or numerous other "controversial" topics. There was plenty of discussions on HN when GitHub was renaming master/slave terminology [0] and when other open source projects were doing the same. Essentially they break the HN guidelines when they 'like' if it is a company / person they 'like'. On the other hand, when a topic attacks their 'narrative' or attacks their side of the argument from someone they don't like, they downvote, flag and censor and make sure it is never seen. Even if it is factual and has evidence. [0] https://news.ycombinator.com/item?id=23518123 https://news.ycombinator.com/item?id=23518123
- loopz 6y agoI find this on hacker news by time site. These discussions become inflated by politics, so are moderated.
- aaron695 6y agoBecause we are not children. We understand the idea training data can affect the results. OP has no evidence for the incendiary accusation and Twitter denies it. https://twitter.com/ZehanWang/status/1307461285811032066?s=20 https://twitter.com/ZehanWang/status/1307461285811032066?s=2...
- bJGVygG7MQVF8c 6y ago> that end up being racist Conditional on the relatively novel definition of racism, this is correct. I would love to see this novel definition of racism used more consistently and thoroughgoingly — to encompass all the manifold races, not just the 19th-century "continental races" taxonomy.
- corrys 6y agoThis assessment sounds correct. The silver lining here is that engineers will pay more attention to algorithm biases in the future.