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So which #DeepLearning model you choose changes the bias introduced. We show that older models like Alexnet are worse compared to newer models like resnet. So that's a good thing. @BeidiChen @Anshumali_ @animesh_garg @jankautz @NvidiaAI
It's important for our #AI leaders to acknowledge that #DeepLearning makes it harder to deal with bias. Models themselves introduce bias. It's important for our leaders not to be so dismissive of deep work happening in this area. They should listen and learn from others.
Do they introduce bias, or as you said before, they amplify the bias present in the data? If it’s the former, I would appreciate if you could point me to some work on this.
We induce our subjectivity to #AI entities, and in our opinion, we aren't sure that it is something entirely wrong, it's the base of intelligence.
#simulation
We ignore the truly significant, we consider it a problem and it is, in our opinion, the opposite.pic.twitter.com/LNbAeolrHs
Isn't Yann talking about social bias, like racial or gender disparity in ML outcomes? The bias you reportt is totally real, but has no reason to equate to recognisable social bias (vs other random bias). Or are you saying that social bias is consistently identified by hardness?
The two are related certainly, and further research needs to be done to understand it.
Thank you ! A good reminder not to downplay the effects of the decision making in ensuring fairness. Is it accurate to say that any decision criteria is potentially biased (adds disparate treatment), including ones that aren’t learned, and the work is to understand and mitigate?
And as you & others have said, this is another ex of why diversity in tech is so crucial — if AI leaders were more diverse & had to frequently deal w bias, black box models would’ve been a nonstarter
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