I have found our original discussion, it was 57 days ago. So let's do another analysis of their timeline two months ago (December+November, for example). But this time, you tell me how to categorize their tweets.
Maybe from the last two you can also see why perhaps simply counting people is not the best metric - a single group photo could distort everything.
You mention you work in statistics, so tell me how you would approach the problem?
Also maybe I didn't express it clearly enough, I didn't look at the whole timeline, only the media timeline: https://twitter.com/lifeatgoogle/media (which is media they posted, pics and videos).
I also didn't claim I can tell bias simply by looking, I said it was my impression they don't want to hire white men anymore. If I was convinced I could tell simply by looking, I wouldn't agree to do an analysis.
As I said - I am sure they still hire white men, but that particular account (and some of their other publications) give the impression they are not interested in that anymore.
And they claim to represent Google's demographic, not the demographics of the general population, so contrary to what you say my numbers do show bias against white men. Not as extreme as I perceived two months ago, but still.
If your company is 70% men and you only report about what women are doing, it is revisionist.
Imagine if all stories about WWII would only be about female soldiers. The impression would be women fought and died for our freedom, all the sacrifices of men would be forgotten. That is revisionism.
>I am sure they still hire white men, but that particular account (and some of their other publications) give the impression they are not interested in that anymore.
Really? What evidence do you have to back up your impression? Why do they give you that impression?
>You mention you work in statistics, so tell me how you would approach the problem?
I'd measure first and then debate my conclusions based on that measurement - and in an area I hadn't measured - I wouldn't spout my impressions or claims - they wouldn't be useful.
Like this thing, which you said one day, not fifty-seven days, ago:
>Huh - that Twitter feed obviously is tilted towards only showing women and PoCs as engineers.
The twitter feed isn't about only women, or only PoCs. You did the measurement yourself.
You seem to still be holding onto your impressions with contrary quantified evidence in front of you. Where's the stuff that supports you?
Another test could be if there are stretches of time when the account gives the impression, that is length of tweet chains without any white men.
I have explained various flaws of my little analysis, but you prefer to ignore them and claim I have provided evidence for your opinion. Yet you work as a statistician :-(
I give up for now, unless you come up with a proper measurement suggestion.
You want me to suggest a measurement method for a claim you've made. Unfortunately for you, for claims you make, you are responsible for questions of methodology surrounding the evidence you use to support it.
You've demonstrated that you'll ignore the best evidence you have in order to hold an impression that you intuit.
>Huh - that Twitter feed obviously is tilted towards only showing women and PoCs as engineers.
Wrong. You've measured this and you are now saying that one test was flawed. The claim it is "obvious" therefore makes no sense.
>It is not just a "guess" - you can look at that feed and check for yourself.
You looked at the feed yourself and were unable to provide evidence it was biased.
For example, should this picture count? It is not somebody working at Google, just a stock image of somebody working in the food industry: https://twitter.com/lifeatgoogle/status/813410799725215744
And how should pictures like this be counted: https://twitter.com/lifeatgoogle/status/800818789122129926 or this https://twitter.com/lifeatgoogle/status/812002215267139584
Maybe from the last two you can also see why perhaps simply counting people is not the best metric - a single group photo could distort everything.
You mention you work in statistics, so tell me how you would approach the problem?
Also maybe I didn't express it clearly enough, I didn't look at the whole timeline, only the media timeline: https://twitter.com/lifeatgoogle/media (which is media they posted, pics and videos).
I also didn't claim I can tell bias simply by looking, I said it was my impression they don't want to hire white men anymore. If I was convinced I could tell simply by looking, I wouldn't agree to do an analysis.
As I said - I am sure they still hire white men, but that particular account (and some of their other publications) give the impression they are not interested in that anymore.
And they claim to represent Google's demographic, not the demographics of the general population, so contrary to what you say my numbers do show bias against white men. Not as extreme as I perceived two months ago, but still.
If your company is 70% men and you only report about what women are doing, it is revisionist.
Imagine if all stories about WWII would only be about female soldiers. The impression would be women fought and died for our freedom, all the sacrifices of men would be forgotten. That is revisionism.