Saturday, December 12, 2020

At A Deeper Political Level Gebru's Paper Said Google Machine Learning Creates More Harm Than Good

Gebru Called Into Question Google's Reputation  Based on the leaked email, Gebru's research says that machine learning at Google (the core of Google's products) creates more harm than good. Somebody finally figured out there that if she is effective in her role, she would be calling into question the ethical standing of Google's core products. If a corporation does ethics research but is unwilling to publicize anything that could be considered critical, then it's not ethics research, it's just peer-reviewed public relations. 

Google miscalculated with Gebru. They thought her comfy paycheck would buy her reputational complicity. Like a typical diversity hire at Corporation X, Gebru was supposed to function as a token figleaf and glad hander among snowflakes who might otherwise ask hard questions. Now Google couldn't just tell her that she was hired to be the good AI house negroe, could they?

Google wants the good narrative of "internal ethics research being done" They want to shape that narrative and message about all of "the improvements we can make" whatever it takes so that questions about their products don't effect their bottom line.  With internal ethics research you have access to exponentially more data  (directly and indirectly, the latter because you know who to talk to and can do so) than any poor academic researcher. 

The field has AI Ethics research teams working on important problems (to the community as a whole). These teams are well funded, sometimes with huge resources.  Now to get the best out of this system, the researchers just need to avoid conflicts with the company core business.  In the case of Gebru's paper,  it could have been reframed in a way that would please Google, without sacrificing its scientific merit. Shaping the narrative is extremely important in politics, business, and ethics.

 And Openly Flouted Managerial Authoriteh  Some are critical if machine learning SVP Jeff Dean for rejecting her submission because of bad "literature review", saying that internal review is supposed to check for "disclosure of sensitive material" only. 

Not only are they wrong about the ultimate purpose of internal review processes, they also missed the point of the rejection. It was never about "literature review", but instead about Google's reputation. Take another look at Dean's response email

It ignored too much relevant research — for example, it talked about the environmental impact of large models, but disregarded subsequent research showing much greater efficiencies. Similarly, it raised concerns about bias in language models, but didn’t take into account recent research to mitigate these issues. Google is the inventor of the current market dominating language models. Who does more neural network training using larger data sets than Google? 

This is how and why Gebru's paper argues that Google creates more harm than good. Would you approve such a paper, as is? This is being kept to the paper and the email to the internal snowflake list - we don't need to examine her intention to sue Google last year, or calling on colleagues to enlist third-party organizations to put more pressure on Google.

Put yourself in Google's cloven-hooved shoes. 

Gebru: Here's my paper in which I call out the environmental impact of large models and raise concerns about bias in the language data sets. Tomorrow is the deadline, please review and approve it. 

Google: Hold on, this makes us look very bad! You have to revise the paper. We know that large models are not good for the environment, but we have also been doing research to achieve much greater efficiencies. We are also aware of bias in the language models that we are using in production, but we are also proposing solutions to that. You should include those works as well.

Gebru: Give me the names of every single person who reviewed my paper otherwise I'll resign. Throw on top of this the fact that she told hundreds of people in the org to cease important work because she had some disagreements with leadership. 

Google: You're Fired!!! Get Out - We'll Pack Your Shit And Mail It To You!!!!


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