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I disagree. It's difficult to understand and fix bias through #AI algorithms for #DeepLearning These are highly non-linear blackbox models. they amplify biases. many works show that trying to fix superficially is like putting lipstick on a pig. We need fresh thinkinghttps://twitter.com/ylecun/status/1203211859366576128 …
In a recent work we report an intriguing finding to detect bias : assess hardness of different samples for #DeepLearning model. It's a first step. Using it to fix bias is much harderhttps://twitter.com/animaanandkumar/status/1203090855097057280 …
More importantly our work shows that #DeepLearning model is deciding which samples are hard for it to classify and introducing bias. Here I use bias to mean disparate treatment not statistical bias. Model leaves harder example with worse accuracy and more vulnerability to noise
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.
CS folks also need to go beyond bias as a test to engage with complex affordances and constraints and a general deeper understanding of social power in daily behavior and exchanges :)
Techno centrism assumes it's plain and simple to fix deep societal issues. Along with it comes disdain for humanities : it's too difficult to fix biases in people, so let's not even bother. My experience has taught me otherwise.
Last year when I started #protestNIPS to push for name + cultural change at #neurips in beginning it was bimodal : trolls vs supporters. But over time, I had lots of people who had a change of heart. They told me they were not aware how badly women and minorities were treated.
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