Let's take Timnit's paper for example. She found out that Google had been using a biased language model in search. The bias was like - 'male' association with 'doctor' as opposed to 'woman' association to 'nurse'. But she didn't show how this is harming anyone in a concrete way. Just theoretical. And then she offered no solution, just blaming the work of others, using her paper as a soap box to raise scandal and make herself holier than thou.
She routinely offered suggested methods, experiments, and even new datasets [1] that fixed what she saw as wrong. She does have a fair bit of practical ML experience as shown in the computer vision papers in her publication list.
1. Buolamwini, Joy, and Timnit Gebru. "Gender shades: Intersectional accuracy disparities in commercial gender classification." Conference on fairness, accountability and transparency. 2018.
> We developed the Pilot Parliaments Benchmark (PPB) to achieve better intersectional representation on the basis of genderand skin type. PPB consists of 1270 individuals from three African countries (Rwanda, Senegal, South Africa) and three European countries (Ice-land, Finland, Sweden) selected for gender parity in the national parliaments.
A dataset of 1270 images is hardly a breakthrough, the kind I expect to see in a small university project. But it doesn't lead to better models because it's not nearly large enough to train on. What it can do is rate existing models. Basically - useful to critique, not to improve.
A small nitpick: why just two races in a de-biasing dataset? Where are the Asians?
Hiring someone (perhaps with aspirations to buy their loyalty?) entails risks. Instead of pulling funding from the "independent think-tank", google now finds itself in the midst of a potential discrimination/political scandal involving an employee.
To roll with the example “man” ~ “doctor”, “woman” ~ “nurse”, the harm is having a giant and widely used search engine reinforce baseless gender biases, ie that there is no underlying reason why women should be nurses and men doctors. What is the harm you may ask? The harm may be subtle, eg being surprised when you find out your next doctor is a woman or your next is a man. It could suppress career choices and aspirations, and it could even be financial, eg reinforcing systemic pay gaps.
You’ve essentially created a fictional data set because it’s biased due to the underlying prejudice (preconceived opinion that is not based on reason or actual experience) that men ought to be nursing more, despite that not being reality.
We’re in a strange situation where we have large concerted efforts by activists to inject fiction in to our facts (whatever the medium) with the aim of distorting perceptions in such a way as to some how correct what they perceive to be injustice in the real world.
A large part of them believe in other delusions such as "silence is violence", coming to work on time is "Whiteness", and "objective, rational" thinking is normalized racism.
In their deranged minds, the ends justify their means, so they can be the antithesis of their being because their crusade is holy, and just.
That's the complete opposite of being a researcher.
Why is the aim of exposing ideology not worthy of being researched in your opinion?
The belief that research has to involve activism has been demonstrated by the individuals under discussion. I cite that this is taught as a valid position in the field.
Political motivation of scientific activity is worthy of research. Both sides involved in the conflict at google may be described as demonstrating political, and apparently opposing, viewpoints.
"Emancipatory research: Research that exposes underlying ideologies in order to liberate those oppressed by them." (Zina O'Leary textbook, used by many schools, over 2K citations for multiple editions)
So if a corporate behemoth such as Google wants to ethicswash itself by hiring individuals with the above approach, what outcome did it expect?