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Professor Iris Bohnet of Harvard wrote a fascinating book called "What Works" that you might find interesting. What I really like about the book is right from the jump Bohnet basically says all this "diversity training" and "PC culture" stuff is a waste of time and an uphill battle. We all like to pretend we're unbiased but Bohnet tells the truth from the outset: everyone has biases. The book focuses on systems that emphasize equal opportunity and remove some of those inherent biases we all have.[1] Bohnet's premise is that we'll get better results if we stop designing systems with the faulty assumption that most people/systems/institutions aren't biased in some way or another.

There's a chapter about "blind auditions" that Orchestras began to implement in the 1970's that you might find interesting.[2] After reading that chapter I remember wondering how much we're overthinking a lot of these issues and whether we might be overlooking some pretty easy changes that could have a significant impact.

One interesting fact from the blind auditions study was that even without being able to see the musician they were able to demonstrate bias (likely unconscious) when women wore high heels to their audition because the sound gave away their gender. It's really fascinating some of the ways we unconsciously "judge" those around us.

[1]https://hbr.org/2016/07/designing-a-bias-free-organization

[2]https://www.theguardian.com/women-in-leadership/2013/oct/14/...

Couldn’t be further from most diversity training I’ve been in - the idea is to identify and find ways to work around bias, not try to “cure” it.

I know that as a result of diversity training and guidelines I am 10x as cautious about my behavior at work in front of women as with just men. That means there is a camaraderie that the men have that the women are left out of. When I worked on Wall Street this divide didn’t exist but there was definitely a culture of sexually hostile work environment and sexual hatrassment.

There are many, many types of camaraderie shared amongst men that have absolutely nothing to do with being sexually hostile towards women.

It's not being hostile, it's more than that. For example, if I invite my men co-workers to go swimming then there is absolutely nothing wrong with that. But if I invite my women co-workers, then it could be interpreted in a bad way.

This is really interesting because I've worked on Wall Street as well and I know exactly what you mean but have never really been able to articulate the difference you've just pointed out. Working on Wall Street I had lots of male friends, not just colleagues. For the most part I felt included and was treated as competent. I also dealt with a fair amount of sexual harassment. Most of it was under the guise of joking/"locker room talk" that I just ignored. Sometimes, particularly from the older men, it was more physical and made me feel very uncomfortable.

The allegations against Google actually really caught me off guard because, in my experience, a lot of men in Silicon Valley seem like they're almost too... progressive (if that's the right word) to overtly harass their female peers the way men on Wall Street sometimes do. On the other hand I really struggle with inclusion in tech in a way that I never did in finance. I often feel dismissed by my tech colleagues. Both personally and professionally. Experiencing that sort of divide is much more difficult to explain and, to your point, much more difficult for me to navigate. Not to diminish women who have been sexually harassed at work, I'm one of them, but this silent divide takes a much bigger toll on me personally.

Thank you for pointing it out.

It’s simple - either people act however they want, or they watch their behavior. In the former they develop stronger bonds with each other. In the latter they are more respectful of each other.

thanks for recommendation! An empirical approach to this is great, I think everyone should try to optimise their hiring process.

As Prof. Bohnet herself points out, the HR dept. should do more A/B testing of its practices instead of relying on the gut feel. Hence the gradual increase in the prevalence of "people analytics" (apart from allure of sucking in all the data-points you can).

But there are a few problems: 1. A/B testing in talent mgmt practices is costly in terms of time-investment. - Final, long term impact of such experiments are not visible to the org for, well, a long while. Sure, may be some short term impacts like increased diversity - may be visible in the short term. But is that worth all the experimentation for all the firms except the largest ones like FANG?

	   The experiment cost is probably not worth the value for the smaller orgs.

2. Vetting people for skillsets and qualifications only, masking the personal details - like in the OP on Helsinki, is a good start. But it does not address the biases on the historical disadvantages like not coming from a pedigreed organization because of not having been to a pedigreed school. Think of being ranked lower in the applications to Netflix because you are not coming from Google because you didn't go to Stanford. Blind -audition technique would be the best in situations like these as the focus would be on demonstrating the skill needed and not the "qualifications" (read - pedigree).

Such skill tests can probably be done easily for programming and other situations but a lot of roles do not have a strictly defined evaluation criteria as the KPIs are...well... fuzzy. Or at least not something which can be demonstrated in a short "test". Think sales / pro-management. You can not demonstrate in a test if you are a good enough sales person who can meet the revenue projections ("sell this pencil to me" doesn't count).

Issue here is lack of a easily reproducible and reliable signal for your future success for such roles, thereby increasing the dependency on past history - and by association, your pedigree.

Of course there will be other issues like isolating performance factors into "chance" and "skill" in a noisy dataset that is usually available, if at all. Or hiring mindset which focuses on the required skillset (what is needed to get the job done) but the "best person" for the job which muddies the waters by adding evaluation factors which are really not material to performance on the job at hand.

But if the cost of experimentation can be reduced by devising quick tests for generating reliable signals independent from historical background, it would be awesome!

I used to recruit for tech start ups and can confirm that school is just about always the most important thing to get you in the door, particularly early on in your career.

It's something that I fought against (I have a technical background and had the advantage of being able to evaluate technical skill somewhat independently), and did well because of it.

There were a few candidates that I brought in that I was reprimanded for - physically brought into our partners' office to be told off - only to have that candidate receive multiple job offers from our clients. The partners weren't qualified to really evaluate technical skill, but knew what candidate qualities that a start up is willing to pay a recruiter's fee for.

At the end of the day, my candidates were exceptional for having risen above their alma maters' reputations, and the Ivy league grads are more consistently good/qualified.

When hiring, time is such an important factor. Many engineering teams are unwilling to take the chance on an a candidate that graduated from unfamiliar school because their hiring process is weak and/or inefficient. Or they waited too long to hire, and need someone "yesterday". At least they're more likely to end up with a good candidate by using the school as the qualifier, even if it costs them more because of the candidate's demand.

TL;DR : The 'top' candidates' values are artificially inflated as a result of high demand, which itself is due to ill-conceived and insufficient hiring processes across most companies.

Recruiting is about minimizing false positives, not maximizing sensitivity.

Even assuming that there is some small enough set of great schools to use them for that signaling as Google traditionally did -- I do not think that's a rational assumption, but that's another discussion -- then at least in the USA you should also know the candidate's parents' financial situation and whether their parents went to the same elite schools.

For starters, because it's a lot easier to "get in" if you have no trouble paying for it, and you have your "legacy admissions."

If you really think XYZ is that great, you should really want to hire the people who fought to get in, fought to succeed, and did.

I agree with the cynicism about choosing based on school. But it does work to some extent. If everyone agrees that XYZ/ABC schools/companies are the best, then rationally students will try hard to get in to those schools. Therefore you can get most of the best by filtering for that history. Sucks if you didn't care about grades when younger but actually turned out smart, but most works.

(No I didn't go to a famous school)

That works for some cases, but not where a candidate's parents also went to the same school or where the parents can and do donate a lot to the school. At most of the schools we're discussing, both situations give the student a boost in admission and in passing classes regardless of effort.

(Disclosure: I went to a well-known school, but without either of the parental benefits I just described. My parents helped in other more indirect ways throughout my upbringing, but they didn't come from a wealthy or connected family background.)

No suggestion, but an anecdote that illustrates a false negative like this.*

Stack Overflow's got an Azure SRE spot open, asking for experience with PowerShell DSC and Terraform, among others. The main goal for the role appears to be scaling their enterprise offering.

I've worked as both a sysadmin and a dev for years now, and used both to manage sizable deployments in public clouds, both Azure and others, mostly for enterprise software companies. This is a mildly specialized skillset (though getting less specialized by the day), and I figured I'd get at least a screening call.

I didn't even make it through the automated resume screening, and if I had to guess it's because I don't have a degree, much less a CS degree.

* On the off-chance my employer reads this, this was more of an exploratory application than me actively hunting for other opportunities.

I completely agree with this.

The problem with bias is that it has affected peoples opportunities in the past. This will be reflected in a CV.

I'm not sure the inclusion of name / age / etc will solve this problem, so Helsinki might still be on the right track.

It feels like the solution is to intentionally introduce a reverse bias towards these candidates. But that idea does sit well... Also interested in any other suggestions here.

Yes, The other information is a job application, at least you can argue that somebody could make a decision based on those information. Whether decision process is just is obviously arguable. If name, age is totally irrelevant to a job, at best it will not be used for selection, while there is much risk that it will be used for discrimination consciously or not.