Maybe the lesson here is that people doing unethical things will take the opportunity to do more unethical things, like not pay you, and it may well be in your self interest to apply your ethics when choosing a job? Upwork doesn't look great here, but seems like it's hardly the whole problem.
Isn't age a protected class though? At least in the US, discrimination over 40 is covered by the EEOC.
Grrr... or that's what they, and probably the courts, would say.
The OP calling it "discrimination laundering" had it right.
The courts are the ones that created the doctrine of "disparate impact." A criterion can be discriminatory even if it doesn't look discriminatory on its face. And, importantly, it does not require intent to discriminate, only impact.
Someone with a better grasp on the particulars here please chime in :)
There is nothing inherent in "being black" that leads to greater basketball performance. Also, define "black," good luck.
Being tall, maaaaaybe. But not perfectly so, and there's no useful correlation between tallness and race, considering that the NBA generally just selects for the most skilled basketball players.
Then you get into all the cultural influences that totally shoot any genetic arguments out of the sky, and really just makes trying to do any, uh, science, around genetics and the NBA pointless. In my experience (as a southerner) it's typically just a sort of dog whistle kind of psuedoscientific attempt to set the groundwork that race can be related to other things, such as likelihood to be a criminal, hence why I jump on this argument whenever I see it. Apologies if that wasn't your intent.
In short, "race" (good luck defining even one) is not related to job performance.
How so universities define someone as black?
First, what do you mean by "black people?"
First, from your link:
>Our data also provide evidence for shared ancestry among geographically diverse hunter-gatherer populations (Khoesan speakers and Pygmies).
Would we say that pygmies, being considered black, would be likely to make it into the NBA?
Second, can we guarantee that people who self identify black, would agree that the definition of someone else's "blackness" is legitimate? Whose definition do we "choose" to then move ahead with "and these sorts of black people are more likely to get into the NBA?" Given that it's not well agreed upon (is Obama black? Steph Curry? Not as cut and dry as you might assume. Are they "black enough" to have NBA advantage?)
Third, based on your tallness: why draw the line as 6'2"? There are shorter players that were Great. So, how do you deal with (relatively frequent) exceptions? "Tall people are more likely to get into the NBA, except when theyre short but grew up next to a court" ? And the tall People that aren't good at basketball? "Tall People are good at basketball except when they're not" ? And when they're too tall to the point that it's detrimental to their health and can't even play basketball?
Fourth, arbitrary. Exactly. It is arbitrary, and thus I argue scientifically impossible to have a rational discussion about blackness and its influence on likelihood to enter the NBA. Or, rather, develop an effective predictor algorithm based on genes alone.
We could have interesting sociological conversations about basketball courts in predominantly black neighborhoods, black NBA stars inspiring black kids, self-propagating cultural memes about blackness and basketball skills, etc. But we won't get any really good information that allows us to say something like "black people are better at basketball" to where it will be true.
Regarding pygmies, they are a different cluster from the highly homogenous cluster of Niger Cordofian from which current African American population in America descends. From the article I cited: "High levels of heterogeneous ancestry (i.e., multiple cluster assignments) were observed innearly all African individuals, with the exception of western and central African Niger-Kordofanian speakers (medium orange), who are relatively homogeneous at large K values(Fig. 5C and fig. S15).)".
I see you are also a bit disingineous if you dont think there is a high correlation between athletic abilities and genetics. Gimme a competent geneticist, 200 hundred genomes of 15 years old boys: 100 African Americans and 100 Guatemalans, shortest dudes in the Americas. I am pretty confident that I can pick 2 teams of 5 kids just by their genome and pick the correct winner of a basketball game between them with high probability. Yeah I cannot predict individuals outcome(yet) but population wise I think it is not a crazy stretch.
On the other hand I see your point that there is no crystal clear boundary between any human group, but if you take that position you have to be consistent with it. What I mean is that I assume you are not happy with an universal definition of black (I can understand and even agree with that). But then you are forced to admit the concept of White privilege is vague too. Same reasoning. You could say "White" is a cultural concept, but I could say the same regarding "Black" so we are back to part 1.
Again I suspect we have more points in common that differences regarding this topic, I just wanted to put out there some considerations.
As all parts of culture, the concept of white privilege is extremely vague, and I imagine white Milwaukee trailer park residents would be a bit offended to be lectured about their privilege. Hence why I would prefer we spend our time focusing more on socioeconomic factors of humanity rather than genetics - it just seems more useful.
I may return to edit this if I have time today but if not thank you for this discussion.
There's a difference between acknowledging some correlations, and using them as statistical tools in the job market. The latter is thankfully illegal or at least frowned upon.
At what point does a correlation become useful as a statistical tool?
If you look at hockey, it is mostly white. That doesn't mean being white gives you an advantage in playing hockey. It just means way more white people enjoy hockey compared to black people.
I suspect it's more that playing hockey implies a certain socioeconomic stratum (hockey is an expensive sport).
The sport is a VERY large time and finance drain on a family.
Rec-league hockey requires an ice rink and time on it. Ice rink time is highly demanded and you may be getting it at 6:00AM or 11:00PM. You also need to coordinate teams at those times. And if you want any practice time-you may be getting that at 3:00AM in the morning (not joking).
And, for all of that, you get the joy of shelling out lots of money for the privilege of using the ice on the graveyard shift.
Practically every high school and elementary school has a basketball court--that's probably 10,000+ in Canada. It doesn't count any associated with public parks, etc.
Contrast that with ice rinks which are probably about 1000. You have at least a factor of 10 differential--probably more. In addition, ice rinks are vastly higher outlay to keep running.
So, that means real money to use the rink when using a basketball court is, at worst, a nominal fee. Longer travel to play against another team if you're in a league. Fewer coaches which need to be paid more. And, your practice times are in the "dead zones" from 11:00PM to 6:00AM in the morning as the ice rink is looking to make real money from 3:00PM to about 11:00PM.
Trait conscientiousness and IQ predict job performance. Stop being Luddites with this technology.
Regardless of the original intent, that's a request to launder a protected class into an opaque "work performance" metric. Also, while they are not on the unfortunately short list of protected classes, using demographics like age is probably going to create similar discrimination problems.
If you want to know about job performance, use data that is at least theoretically related to someone's performance. While machine learning is going to find some sort opf signal in whatever pile of data you feed it, that doesn't mean it actually predicts something complicated like job performance. When you feed it a bunch of general demographics, it's going to find one of the signals that we know are present throughout existing demographic: the institutional discrimination and similar problems of the past.