It's
to poke around and see what biases the system picked up from the training data.
to think about actual applications, though.pic.twitter.com/VJ9y9yxz2D
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It's
to poke around and see what biases the system picked up from the training data.
to think about actual applications, though.pic.twitter.com/VJ9y9yxz2D
Maybe it's not fair to try single words when the model is designed for use on comments, so let's try things that could be entire comments
pic.twitter.com/z57y5KPyU0
Wouldn't christian/muslim be a more appropriate comparison that christian/islam?pic.twitter.com/2Wi30MQD6m
Amazing how a leading tech company in 2017 came up with a system no better than a 1990s Yahoo chatroom word filter _and still released it_
This is a service trained to guess if a comment on the internet is toxic or not, it is not designed to use ML to define if a single word is toxic. Think about the comments you've seen on YouTube/etc that have the words you're using and then think about _who_ types them.
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