I agree with @zicokolter so many young people are extremely stressed about desk rejects at @NeurIPSConf I urge the chairs to remove this to ease stress on young people @RaiaHadsellhttps://twitter.com/zicokolter/status/1268040743198887936 …
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Replying to @AnimaAnandkumar @zicokolter and
As an AC I feel extremely uncomfortable doing this, as I also felt as Senior PC in IJCAI. I tweeted about this when it was first proposed months ago. A paper should be desk rejected based on its own merit or lack of it, not because it is in bottom 20%
3 replies 2 retweets 13 likes -
Replying to @ashipra @zicokolter and
I have a student extremely concerned about talking about societal bias in
#AI models because that may lead to desk rejection@NeurIPSConf this is now more power in hands of a few. Not good for@InclusionInML2 replies 3 retweets 14 likes -
Replying to @AnimaAnandkumar @ashipra and
Why would this lead to desk rejection? The subject area of the paper is not related to probability of desk reject. NeurIPS has a top level category on 'Social Aspects of ML' so this seems both in scope and timely - please encourage your student to submit! https://neurips.cc/Conferences/2020/PaperInformation/SubjectAreas …
1 reply 0 retweets 3 likes -
Replying to @RaiaHadsell @ashipra and
1/ Thanks for responding
@RaiaHadsell in theory there should be freedom of speech and we should be able to state that there are societal biases. This paper still an algorithms paper that can help alleviate it if used correctly.1 reply 0 retweets 0 likes -
Replying to @AnimaAnandkumar @RaiaHadsell and
2/ the student is worried that it is a political statement to say there are biases in our current
#AI models. The worry is that the AC may desk reject if that view is not shared. May be an AC can make up reasons to reject because they feel a paper is making political statements1 reply 0 retweets 1 like
3/ We have many #AI researchers who don't believe there is any sexism or racism in the world. They dismiss research on fairness and bias in #AI. What if now these are ACs? An algorithms paper saying that there are biases could be rejected because they don't agree
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