And so are all flawed institutions. None of this supports your basic assertion that the risk of misuse or misunderstanding of ML is different in principle from the ways all other techs & processes are subject to misuse or misunderstanding. ‘It’s not auditable’ is not good enough
-
-
I think we need more work on what if it works, besides all the work on how it picks up structural biases that already exist in the data and we know to look for.
Thanks. Twitter will use this to make your timeline better. UndoUndo
-
-
-
Perhaps. But simply asserting something and then ignoring any possible objection does not make for a strong position.
-
So that I'm clear: is the assertion you are objecting to this:"ML is different than trad programs or databases and that it creates (for computation) unique challenges to transparency and auditing?" It's good for me to understand because, honestly, I'd have ranked that as mundane.
- Show replies
New conversation -
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.
It's just not the same as say, adding more variables like heart rate, blood pressure, this and that measurement and running a regression or applying a formula.