If by "algorithms" we mean ML—and that's what we mean in 2019—ML is inherently and intrinsically bound with data, and it's increasingly formally (formally, as in mathematically) clear that problem of bias thus isn't solveable. https://arxiv.org/abs/1903.03862 andhttps://arxiv.org/abs/1609.05807
-
This Tweet is unavailable.
-
This Tweet is unavailable.
-
-
This Tweet is unavailable.
-
In most cases ML 1-surfaces bias; and/or 2-focuses it via feedbackloops; and/or 3-creates new ones by adding the ability to detect things at scale that we couldn't before (not hiring people prone to depression, for example). All those are risks, though 1 is also an opportunity.
3 replies 0 retweets 9 likes
Widespread application of and trust in a potent technology is of course a risk when it's wrong and yes, even when it's right depending on what's being detected. Some of it may aid fixing earlier forms of discrimination. First, take the risk/transition seriously though.
5:39 AM - 24 Mar 2019
0 replies
0 retweets
7 likes
Loading seems to be taking a while.
Twitter may be over capacity or experiencing a momentary hiccup. Try again or visit Twitter Status for more information.