Can fake names create bias in interviewing?

> Nonsense. This comment chain is about diversity hiring practices, which involves gender and race.

My original response to your comment, and every single comment in the subsequent chain, referenced diversity in terms of gender. Every discussion in terms of the share of women in STEM in different countries has been on the basis of gender. All the sources you asked for (and I provided) were in relation to gender.

> So was it the day after that women made up an equal share of university graduates? Or was it a progression over time, picking up more recently in the last few decades?

Women's disenfranchisement ended on August 18, 1920. The date when the 19th amendment was passed. That's where the reference of a century ago comes from, though if you want to be pedantic it is 99 years and 6 months ago. That's what I thought you were referring to with disenfranchisement. You have consistently rejected statistics as evidence of bias, so it seemed to me you were basing this claim of bias on de-jure discrimination against women in the past.

If you're referring to women's disadvantaged position in society, then there's really no answer to that question as it's a subjective judgement. Women long faced restrictions in education and employment. Men long faced (and continue to face) disparities in incarceration, violent death, suicide, workplace injuries and illnesses. How many instances of harassment of equate to one murder of a man? How much of a pay gap are men entitled to if they make up >90% of workplace injuries and deaths? There's no right answer to these questions as these are subjective value judgments.

As far as when women broadly became more or less equal to men, the consensus is that this occurred a few decades after the civil rights movement. At this point women born after the civil rights movement were reaching adulthood. Around the 1990s and 2000s is when women became a majority in university, and reached parity (and eventually became a majority) in workplace participation.

> And is that coincidental with the increase in affirmative action policies. Interesting.

Most of the affirmative actions policies in tech are more recent than that. My company enacted it's aggressive affirmative hiring policies in the early to mid 2010s. By comparison the percentage of women in tech was higher before they experienced large advancements towards equality in the 90s. Women's share of computer science degrees peaked in the early 1980s [1]. So the answer to your question is no. There was no increase in women in computer science that coincided with the affirmative hiring policies.

This is not surprising. When companies discriminate to hire more women, it doesn't change the percentage of women in computer science. The impact of these policies it that companies which discriminate hire a larger portion of the women that choose to go into computer science. The number of women in compute science remains relatively flat, but companies expect to increase their percentages of female tech workers. Every company is competing for the same limited pool of women in tech, so many employ discrimination to try and get an edge over the others.

> I suppose you could argue that women's genes have evolved towards an interest in STEM in the past few decades. I bet you could even find a link or two to share as "evidence".

I have no idea why you think I would claim that women "genes have evolved towards an interest in stem". This is ridiculous. Evolution takes place over millennia, at least. Usually over tens of millennia or millions of years for substantial evolutionary changes.

Not to mention, the idea that women's interest in computing has increased over time is counterfactual. At least I think this is your line of reasoning - you're being awfully ambiguous as to why you'd think that I would claim that "women's genes have evolved towards an interest in STEM over the past few decades. In truth, women's interest in computing has seen some spikes (and these spikes also coincided with spikes in men's participation) but has otherwise been pretty flat over time: https://i0.wp.com/d24fkeqntp1r7r.cloudfront.net/wp-content/u... The source is the National Center for Education Statistics.

These closing statements strike me as argumentum ad hominem.

1. https://www.npr.org/sections/money/2014/10/21/357629765/when...

So my company's diversity policies are bad? Women account for 20% of computing graduates, but 39% of our new grad hires in computing roles

The resukts of our hiring process are very unequal, women are hired at twice the rate. And not only are we not giving preferences equate this imbalance, we are actually deliberately employing discrimination to exacerbate it.

As one of those affected (see my other comments on this thread), I want to be judged on the output of my work and not on my skin color.

The person you’re responding to is clearly motivated by ideology rather than truth. They just happen to be good at generating paragraphs of text which, if you don’t look too closely, resemble a logical argument.

What I mean is that social science research will only ever be able to speak to the samples they are studying. That's usually okay, though, because typically in an experiment with difficult variability, you can gain certainty through replication. With social science, the variability in human beings is so significant that I don't think replication works the same way. I think you would need to replicate studies about a dozen times across different cultures, timeframes, languages, regions, religions, etc just to even begin approaching something like reliable results.

And given the fact that social science has a replication problem (an understatement if I'm right), the entire area of study is suspect.