This is an example of sacrificing the scientific method to make results more politically correct. We've come full circle.
Wrongthink is nothing new, comrade ajwnwnkwos, and is as prevalent as ever.
The output of science has been suppressed throughout history where it didn't fit the narrative of the day. At one period, that was by the Church. Another, it was at the hands of the government. Today, mainly by a self-censoring, everything-must-be-pleasant-and-entertaining society that is very highly prone to fits of outrage.
Inconvenient facts are, after all, inconvenient.
In the specific context of science I’m unsure what self-censoring would even look like. Not only would such conclusions be a relatively small subset (hard to discuss the self-censorship of a graph algorithm) but there is plenty of attention(therefore funding) on both sides to uncover some truth about any political subject.
Saying that global warming is real and caused by humans offends a large chunk of people.
Saying that vaccines are effective and don't cause autism offends a non-trivial amount of people.
Saying that (meat|sugar|fat|veganism|political ideology) is unethical or unhealthy offends pretty much everyone depending on which you pick.
So should we seek to not offend by not saying these things? Or should we strive to uncover the truth behind and change the things we don't like, instead of being offended by the acknowledgement of their existence.
I read your blog post above about "racist" NLP models and I agree it makes sense to debias inputs for certain purposes.
However, many people in this thread aren't talking about adjusting for statistical bias. They're really talking about adjusting for socio-political over/under-representation, which is getting onto pretty shaky ground.
I'd hazard a guess that a model trained on Google News giving "more negative" sentiment for black names is a function of those names appearing more frequently in crime articles, matching the overrepresentation in crime rates. Likewise for Arab/Muslim names, which are presumably disproportionately present in articles on terrorism.
Now, that's obviously statistical sampling bias if you're (somehow) modelling "names I should call my kid".
If you're looking at "names more associated with crime", however, then we shouldn't be eliminating any racial imbalances simply because they make us feel queasy. That's intellectually dishonest, and does everybody a disservice by trying to sweep uncomfortable realities under the rug.
Same goes for gender bias in word embeddings from classical fiction - that is (I assume) a very accurate depiction of social gender imbalance from that period of history. That may or may not be relevant to the question you're asking - but that doesn't make any bias inherently "wrong".
I think the underlying message should be "models only reflect the data they're given and may need to be adjusted for bias depending on the question being asked", not "race and gender is always irrelevant and should always be normalized".