We read the paper that forced Timnit Gebru out of Google

Even someone like Ben Shapiro recognizes a difference between liberals and leftists (https://twitter.com/benshapiro/status/966081078166421504).

Silicon Valley types would hardly be described as leftists. Numerous studies have been done on the attitudes of Silicon Valley founders and execs (https://www.vox.com/2015/9/29/9411117/silicon-valley-politic...). The distinctions are dramatic.

We see that on average, tech founders are less likely to support vs. even Democrats generally (not just progressives):

* Banning the Keystone XL pipeline (60% vs 78%)

* The individual healthcare mandate (59% vs 70%)

* Labor unions being good (29% vs 73%)

This is to say, the average Silicon Valley type, particularly the C-suite exec or founder, tends not to be on the left wing of the Democratic party.

During the 2020 Democratic primary, even the Silicon Valley billionaires who are openly Democratic-leaning donated to candidates who were not to the left of the field (i.e. Elizabeth Warren and Bernie Sanders) (https://www.cnbc.com/2019/08/13/2020-democratic-presidential...):

* Eric Schmidt -> Cory Booker and Joe Biden

* Reed Hastings -> Pete Buttigieg

* Marc Benioff -> Cory Booker, Kamala Harris, and Jay Inslee

* Reid Hoffman -> Cory Booker, Kirsten Gillibrand, Amy Klobuchar

* Jack Dorsey -> Andrew Yang, Tulsi Gabbard

* Ben Silbermann -> Pete Buttigieg

I'm engaging with you in good faith, and because I was intrigued that in a previous comment you mentioned that you live in Spain (though who's to say you're not a US ex-pat). But calling US tech companies "leftist" is a stretch at best.

The point is, I'm not a Google shill.

Still, on this case, on a factual level, the only real dispute is whether this exchange:

"Do X or I quit" "Ok, your final paycheck is in the mail and IT will be in touch to organise equipment returns, effective now"

Is "accepting a resignation" or "firing". Neither side is disputing that this is how it went down.

On an ethical level, again, I'm no fan of Google but Timnit Cebru's previous public actions don't paint her in a good light while Jeff Dean's doesn't have any notable enough to sway my opinion one way or another on his ethical trustworthiness.

So based on that, I (and many others) do end up siding with Google. is that a pro-Google push? is someone co-ordinating this? If they are, they haven't contacted me. Don't mistake the fact that Google is often unpopular here with the idea that no Google action can be supported here without interference

> If you pick option #2 then your model does not reflect real life.

These deep learning models built by corporations are not scientific models, they are engineering solutions, built to solve problems. Reflecting the real world is only useful if it furthers what the company wants to solve. If they for example remove swear words from their training set, that will make them a less accurate model of the world, but make them more useful for building solutions. But it is probably a trade of they would be happy with. We've also seen example of risk scoring application for felons that seem to end up doing racial profiling, because that is what the data seem to indicate makes sense. But that's deeply problematic and runs counter to laws in some places and seem ethically problematic (https://www.theverge.com/2020/6/24/21301465/ai-machine-learn...).

> Bias is by definition the way the model returns results that don't match the real world and real life, in frequency and in proportion.

Getting a good data set without bias is hard, even if you crawl the whole internet like Google does. Not everything is on the internet and there are systematic drivers that make some part of the human condition over represented (English, science, the views of the affluent and educated), and some under presented (small languages, the discourse of people behind the Chinese great firewall, the poor). So just getting a ginormous data set does not fix bias.

> If your goal is to use your model to manipulate and control society based on your own personal criteria, by manipulating it to return results that distort the real world and real life, then call it something else, because bias is not it.

Positive bias is absolutely something that we use, and while it might seem sinister it does not have to be. The example I'm most familiar with a facial recognition technology. Most groups building that ends up with a model that is better at some groups than others. Asian research groups often end up with models that does well with asians and worse with whites, while European groups usually end up with the reverse. In some sense these results do reflect the reality of these groups, most people in Europe is white and most people Asian is asians, so that you training sets ends up like that is not surprising. But no one is happy with these kind of results, and everyone wants to fix that.

To bring it back to speech and text models, let's say you are building a customer service solutions incorporating a deep learning model, the reality might be that you current customer service representatives treat blacks (or people who use "black" dialects), worse than people who sound white. An accurate model built on this data set will then also do that. But is that acceptable? I hope most companies would want to fix that, and be fine with adding some positive bias in their solution.